
# Import Python Libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.style as style
import seaborn as sns
import itertools
%matplotlib inline

# setting up plot style 
style.use('seaborn-poster')
style.use('fivethirtyeight')


# Supress Warnings
import warnings
warnings.filterwarnings('ignore')


# Adjust Jupyer Views
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
pd.set_option('display.expand_frame_repr', False)


# Importing the input files
applicationDF = pd.read_csv(r'E:\Resume Project\1\application_data.csv')
previousDF = pd.read_csv(r'E:\Resume Project\1\previous_application.csv')
applicationDF.head() #calling top 5 rows in application dataset


previousDF.head() #calling top 5 rows in previous dataset


# Database dimension
print("Database dimension - applicationDF     :",applicationDF.shape)
print("Database dimension - previousDF        :",previousDF.shape)

#Database size
print("Database size - applicationDF          :",applicationDF.size)
print("Database size - previousDF             :",previousDF.size)

Database dimension - applicationDF     : (307511, 122)
Database dimension - previousDF        : (1670214, 37)
Database size - applicationDF          : 37516342
Database size - previousDF             : 61797918


# Database column types
applicationDF.info(verbose=True)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Data columns (total 122 columns):
 #    Column                        Dtype  
---   ------                        -----  
 0    SK_ID_CURR                    int64  
 1    TARGET                        int64  
 2    NAME_CONTRACT_TYPE            object 
 3    CODE_GENDER                   object 
 4    FLAG_OWN_CAR                  object 
 5    FLAG_OWN_REALTY               object 
 6    CNT_CHILDREN                  int64  
 7    AMT_INCOME_TOTAL              float64
 8    AMT_CREDIT                    float64
 9    AMT_ANNUITY                   float64
 10   AMT_GOODS_PRICE               float64
 11   NAME_TYPE_SUITE               object 
 12   NAME_INCOME_TYPE              object 
 13   NAME_EDUCATION_TYPE           object 
 14   NAME_FAMILY_STATUS            object 
 15   NAME_HOUSING_TYPE             object 
 16   REGION_POPULATION_RELATIVE    float64
 17   DAYS_BIRTH                    int64  
 18   DAYS_EMPLOYED                 int64  
 19   DAYS_REGISTRATION             float64
 20   DAYS_ID_PUBLISH               int64  
 21   OWN_CAR_AGE                   float64
 22   FLAG_MOBIL                    int64  
 23   FLAG_EMP_PHONE                int64  
 24   FLAG_WORK_PHONE               int64  
 25   FLAG_CONT_MOBILE              int64  
 26   FLAG_PHONE                    int64  
 27   FLAG_EMAIL                    int64  
 28   OCCUPATION_TYPE               object 
 29   CNT_FAM_MEMBERS               float64
 30   REGION_RATING_CLIENT          int64  
 31   REGION_RATING_CLIENT_W_CITY   int64  
 32   WEEKDAY_APPR_PROCESS_START    object 
 33   HOUR_APPR_PROCESS_START       int64  
 34   REG_REGION_NOT_LIVE_REGION    int64  
 35   REG_REGION_NOT_WORK_REGION    int64  
 36   LIVE_REGION_NOT_WORK_REGION   int64  
 37   REG_CITY_NOT_LIVE_CITY        int64  
 38   REG_CITY_NOT_WORK_CITY        int64  
 39   LIVE_CITY_NOT_WORK_CITY       int64  
 40   ORGANIZATION_TYPE             object 
 41   EXT_SOURCE_1                  float64
 42   EXT_SOURCE_2                  float64
 43   EXT_SOURCE_3                  float64
 44   APARTMENTS_AVG                float64
 45   BASEMENTAREA_AVG              float64
 46   YEARS_BEGINEXPLUATATION_AVG   float64
 47   YEARS_BUILD_AVG               float64
 48   COMMONAREA_AVG                float64
 49   ELEVATORS_AVG                 float64
 50   ENTRANCES_AVG                 float64
 51   FLOORSMAX_AVG                 float64
 52   FLOORSMIN_AVG                 float64
 53   LANDAREA_AVG                  float64
 54   LIVINGAPARTMENTS_AVG          float64
 55   LIVINGAREA_AVG                float64
 56   NONLIVINGAPARTMENTS_AVG       float64
 57   NONLIVINGAREA_AVG             float64
 58   APARTMENTS_MODE               float64
 59   BASEMENTAREA_MODE             float64
 60   YEARS_BEGINEXPLUATATION_MODE  float64
 61   YEARS_BUILD_MODE              float64
 62   COMMONAREA_MODE               float64
 63   ELEVATORS_MODE                float64
 64   ENTRANCES_MODE                float64
 65   FLOORSMAX_MODE                float64
 66   FLOORSMIN_MODE                float64
 67   LANDAREA_MODE                 float64
 68   LIVINGAPARTMENTS_MODE         float64
 69   LIVINGAREA_MODE               float64
 70   NONLIVINGAPARTMENTS_MODE      float64
 71   NONLIVINGAREA_MODE            float64
 72   APARTMENTS_MEDI               float64
 73   BASEMENTAREA_MEDI             float64
 74   YEARS_BEGINEXPLUATATION_MEDI  float64
 75   YEARS_BUILD_MEDI              float64
 76   COMMONAREA_MEDI               float64
 77   ELEVATORS_MEDI                float64
 78   ENTRANCES_MEDI                float64
 79   FLOORSMAX_MEDI                float64
 80   FLOORSMIN_MEDI                float64
 81   LANDAREA_MEDI                 float64
 82   LIVINGAPARTMENTS_MEDI         float64
 83   LIVINGAREA_MEDI               float64
 84   NONLIVINGAPARTMENTS_MEDI      float64
 85   NONLIVINGAREA_MEDI            float64
 86   FONDKAPREMONT_MODE            object 
 87   HOUSETYPE_MODE                object 
 88   TOTALAREA_MODE                float64
 89   WALLSMATERIAL_MODE            object 
 90   EMERGENCYSTATE_MODE           object 
 91   OBS_30_CNT_SOCIAL_CIRCLE      float64
 92   DEF_30_CNT_SOCIAL_CIRCLE      float64
 93   OBS_60_CNT_SOCIAL_CIRCLE      float64
 94   DEF_60_CNT_SOCIAL_CIRCLE      float64
 95   DAYS_LAST_PHONE_CHANGE        float64
 96   FLAG_DOCUMENT_2               int64  
 97   FLAG_DOCUMENT_3               int64  
 98   FLAG_DOCUMENT_4               int64  
 99   FLAG_DOCUMENT_5               int64  
 100  FLAG_DOCUMENT_6               int64  
 101  FLAG_DOCUMENT_7               int64  
 102  FLAG_DOCUMENT_8               int64  
 103  FLAG_DOCUMENT_9               int64  
 104  FLAG_DOCUMENT_10              int64  
 105  FLAG_DOCUMENT_11              int64  
 106  FLAG_DOCUMENT_12              int64  
 107  FLAG_DOCUMENT_13              int64  
 108  FLAG_DOCUMENT_14              int64  
 109  FLAG_DOCUMENT_15              int64  
 110  FLAG_DOCUMENT_16              int64  
 111  FLAG_DOCUMENT_17              int64  
 112  FLAG_DOCUMENT_18              int64  
 113  FLAG_DOCUMENT_19              int64  
 114  FLAG_DOCUMENT_20              int64  
 115  FLAG_DOCUMENT_21              int64  
 116  AMT_REQ_CREDIT_BUREAU_HOUR    float64
 117  AMT_REQ_CREDIT_BUREAU_DAY     float64
 118  AMT_REQ_CREDIT_BUREAU_WEEK    float64
 119  AMT_REQ_CREDIT_BUREAU_MON     float64
 120  AMT_REQ_CREDIT_BUREAU_QRT     float64
 121  AMT_REQ_CREDIT_BUREAU_YEAR    float64
dtypes: float64(65), int64(41), object(16)
memory usage: 286.2+ MB


previousDF.info(verbose=True)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1670214 entries, 0 to 1670213
Data columns (total 37 columns):
 #   Column                       Non-Null Count    Dtype  
---  ------                       --------------    -----  
 0   SK_ID_PREV                   1670214 non-null  int64  
 1   SK_ID_CURR                   1670214 non-null  int64  
 2   NAME_CONTRACT_TYPE           1670214 non-null  object 
 3   AMT_ANNUITY                  1297979 non-null  float64
 4   AMT_APPLICATION              1670214 non-null  float64
 5   AMT_CREDIT                   1670213 non-null  float64
 6   AMT_DOWN_PAYMENT             774370 non-null   float64
 7   AMT_GOODS_PRICE              1284699 non-null  float64
 8   WEEKDAY_APPR_PROCESS_START   1670214 non-null  object 
 9   HOUR_APPR_PROCESS_START      1670214 non-null  int64  
 10  FLAG_LAST_APPL_PER_CONTRACT  1670214 non-null  object 
 11  NFLAG_LAST_APPL_IN_DAY       1670214 non-null  int64  
 12  RATE_DOWN_PAYMENT            774370 non-null   float64
 13  RATE_INTEREST_PRIMARY        5951 non-null     float64
 14  RATE_INTEREST_PRIVILEGED     5951 non-null     float64
 15  NAME_CASH_LOAN_PURPOSE       1670214 non-null  object 
 16  NAME_CONTRACT_STATUS         1670214 non-null  object 
 17  DAYS_DECISION                1670214 non-null  int64  
 18  NAME_PAYMENT_TYPE            1670214 non-null  object 
 19  CODE_REJECT_REASON           1670214 non-null  object 
 20  NAME_TYPE_SUITE              849809 non-null   object 
 21  NAME_CLIENT_TYPE             1670214 non-null  object 
 22  NAME_GOODS_CATEGORY          1670214 non-null  object 
 23  NAME_PORTFOLIO               1670214 non-null  object 
 24  NAME_PRODUCT_TYPE            1670214 non-null  object 
 25  CHANNEL_TYPE                 1670214 non-null  object 
 26  SELLERPLACE_AREA             1670214 non-null  int64  
 27  NAME_SELLER_INDUSTRY         1670214 non-null  object 
 28  CNT_PAYMENT                  1297984 non-null  float64
 29  NAME_YIELD_GROUP             1670214 non-null  object 
 30  PRODUCT_COMBINATION          1669868 non-null  object 
 31  DAYS_FIRST_DRAWING           997149 non-null   float64
 32  DAYS_FIRST_DUE               997149 non-null   float64
 33  DAYS_LAST_DUE_1ST_VERSION    997149 non-null   float64
 34  DAYS_LAST_DUE                997149 non-null   float64
 35  DAYS_TERMINATION             997149 non-null   float64
 36  NFLAG_INSURED_ON_APPROVAL    997149 non-null   float64
dtypes: float64(15), int64(6), object(16)
memory usage: 471.5+ MB


# Checking the numeric variables of the dataframes
applicationDF.describe()


previousDF.describe()


!pip install missingno

Requirement already satisfied: missingno in c:\users\sai viswa\anaconda3\lib\site-packages (0.5.2)
Requirement already satisfied: numpy in c:\users\sai viswa\anaconda3\lib\site-packages (from missingno) (1.24.3)
Requirement already satisfied: matplotlib in c:\users\sai viswa\anaconda3\lib\site-packages (from missingno) (3.7.2)
Requirement already satisfied: scipy in c:\users\sai viswa\anaconda3\lib\site-packages (from missingno) (1.11.1)
Requirement already satisfied: seaborn in c:\users\sai viswa\anaconda3\lib\site-packages (from missingno) (0.12.2)
Requirement already satisfied: contourpy>=1.0.1 in c:\users\sai viswa\anaconda3\lib\site-packages (from matplotlib->missingno) (1.0.5)
Requirement already satisfied: cycler>=0.10 in c:\users\sai viswa\anaconda3\lib\site-packages (from matplotlib->missingno) (0.11.0)
Requirement already satisfied: fonttools>=4.22.0 in c:\users\sai viswa\anaconda3\lib\site-packages (from matplotlib->missingno) (4.25.0)
Requirement already satisfied: kiwisolver>=1.0.1 in c:\users\sai viswa\anaconda3\lib\site-packages (from matplotlib->missingno) (1.4.4)
Requirement already satisfied: packaging>=20.0 in c:\users\sai viswa\anaconda3\lib\site-packages (from matplotlib->missingno) (23.1)
Requirement already satisfied: pillow>=6.2.0 in c:\users\sai viswa\anaconda3\lib\site-packages (from matplotlib->missingno) (9.4.0)
Requirement already satisfied: pyparsing<3.1,>=2.3.1 in c:\users\sai viswa\anaconda3\lib\site-packages (from matplotlib->missingno) (3.0.9)
Requirement already satisfied: python-dateutil>=2.7 in c:\users\sai viswa\anaconda3\lib\site-packages (from matplotlib->missingno) (2.8.2)
Requirement already satisfied: pandas>=0.25 in c:\users\sai viswa\anaconda3\lib\site-packages (from seaborn->missingno) (2.0.3)
Requirement already satisfied: pytz>=2020.1 in c:\users\sai viswa\anaconda3\lib\site-packages (from pandas>=0.25->seaborn->missingno) (2023.3.post1)
Requirement already satisfied: tzdata>=2022.1 in c:\users\sai viswa\anaconda3\lib\site-packages (from pandas>=0.25->seaborn->missingno) (2023.3)
Requirement already satisfied: six>=1.5 in c:\users\sai viswa\anaconda3\lib\site-packages (from python-dateutil>=2.7->matplotlib->missingno) (1.16.0)


# Null Value Calculation
import missingno as mn
mn.matrix(applicationDF)

<Axes: >


# % null value in each column
round(applicationDF.isnull().sum() / applicationDF.shape[0] * 100.00,2)

SK_ID_CURR                       0.00
TARGET                           0.00
NAME_CONTRACT_TYPE               0.00
CODE_GENDER                      0.00
FLAG_OWN_CAR                     0.00
FLAG_OWN_REALTY                  0.00
CNT_CHILDREN                     0.00
AMT_INCOME_TOTAL                 0.00
AMT_CREDIT                       0.00
AMT_ANNUITY                      0.00
AMT_GOODS_PRICE                  0.09
NAME_TYPE_SUITE                  0.42
NAME_INCOME_TYPE                 0.00
NAME_EDUCATION_TYPE              0.00
NAME_FAMILY_STATUS               0.00
NAME_HOUSING_TYPE                0.00
REGION_POPULATION_RELATIVE       0.00
DAYS_BIRTH                       0.00
DAYS_EMPLOYED                    0.00
DAYS_REGISTRATION                0.00
DAYS_ID_PUBLISH                  0.00
OWN_CAR_AGE                     65.99
FLAG_MOBIL                       0.00
FLAG_EMP_PHONE                   0.00
FLAG_WORK_PHONE                  0.00
FLAG_CONT_MOBILE                 0.00
FLAG_PHONE                       0.00
FLAG_EMAIL                       0.00
OCCUPATION_TYPE                 31.35
CNT_FAM_MEMBERS                  0.00
REGION_RATING_CLIENT             0.00
REGION_RATING_CLIENT_W_CITY      0.00
WEEKDAY_APPR_PROCESS_START       0.00
HOUR_APPR_PROCESS_START          0.00
REG_REGION_NOT_LIVE_REGION       0.00
REG_REGION_NOT_WORK_REGION       0.00
LIVE_REGION_NOT_WORK_REGION      0.00
REG_CITY_NOT_LIVE_CITY           0.00
REG_CITY_NOT_WORK_CITY           0.00
LIVE_CITY_NOT_WORK_CITY          0.00
ORGANIZATION_TYPE                0.00
EXT_SOURCE_1                    56.38
EXT_SOURCE_2                     0.21
EXT_SOURCE_3                    19.83
APARTMENTS_AVG                  50.75
BASEMENTAREA_AVG                58.52
YEARS_BEGINEXPLUATATION_AVG     48.78
YEARS_BUILD_AVG                 66.50
COMMONAREA_AVG                  69.87
ELEVATORS_AVG                   53.30
ENTRANCES_AVG                   50.35
FLOORSMAX_AVG                   49.76
FLOORSMIN_AVG                   67.85
LANDAREA_AVG                    59.38
LIVINGAPARTMENTS_AVG            68.35
LIVINGAREA_AVG                  50.19
NONLIVINGAPARTMENTS_AVG         69.43
NONLIVINGAREA_AVG               55.18
APARTMENTS_MODE                 50.75
BASEMENTAREA_MODE               58.52
YEARS_BEGINEXPLUATATION_MODE    48.78
YEARS_BUILD_MODE                66.50
COMMONAREA_MODE                 69.87
ELEVATORS_MODE                  53.30
ENTRANCES_MODE                  50.35
FLOORSMAX_MODE                  49.76
FLOORSMIN_MODE                  67.85
LANDAREA_MODE                   59.38
LIVINGAPARTMENTS_MODE           68.35
LIVINGAREA_MODE                 50.19
NONLIVINGAPARTMENTS_MODE        69.43
NONLIVINGAREA_MODE              55.18
APARTMENTS_MEDI                 50.75
BASEMENTAREA_MEDI               58.52
YEARS_BEGINEXPLUATATION_MEDI    48.78
YEARS_BUILD_MEDI                66.50
COMMONAREA_MEDI                 69.87
ELEVATORS_MEDI                  53.30
ENTRANCES_MEDI                  50.35
FLOORSMAX_MEDI                  49.76
FLOORSMIN_MEDI                  67.85
LANDAREA_MEDI                   59.38
LIVINGAPARTMENTS_MEDI           68.35
LIVINGAREA_MEDI                 50.19
NONLIVINGAPARTMENTS_MEDI        69.43
NONLIVINGAREA_MEDI              55.18
FONDKAPREMONT_MODE              68.39
HOUSETYPE_MODE                  50.18
TOTALAREA_MODE                  48.27
WALLSMATERIAL_MODE              50.84
EMERGENCYSTATE_MODE             47.40
OBS_30_CNT_SOCIAL_CIRCLE         0.33
DEF_30_CNT_SOCIAL_CIRCLE         0.33
OBS_60_CNT_SOCIAL_CIRCLE         0.33
DEF_60_CNT_SOCIAL_CIRCLE         0.33
DAYS_LAST_PHONE_CHANGE           0.00
FLAG_DOCUMENT_2                  0.00
FLAG_DOCUMENT_3                  0.00
FLAG_DOCUMENT_4                  0.00
FLAG_DOCUMENT_5                  0.00
FLAG_DOCUMENT_6                  0.00
FLAG_DOCUMENT_7                  0.00
FLAG_DOCUMENT_8                  0.00
FLAG_DOCUMENT_9                  0.00
FLAG_DOCUMENT_10                 0.00
FLAG_DOCUMENT_11                 0.00
FLAG_DOCUMENT_12                 0.00
FLAG_DOCUMENT_13                 0.00
FLAG_DOCUMENT_14                 0.00
FLAG_DOCUMENT_15                 0.00
FLAG_DOCUMENT_16                 0.00
FLAG_DOCUMENT_17                 0.00
FLAG_DOCUMENT_18                 0.00
FLAG_DOCUMENT_19                 0.00
FLAG_DOCUMENT_20                 0.00
FLAG_DOCUMENT_21                 0.00
AMT_REQ_CREDIT_BUREAU_HOUR      13.50
AMT_REQ_CREDIT_BUREAU_DAY       13.50
AMT_REQ_CREDIT_BUREAU_WEEK      13.50
AMT_REQ_CREDIT_BUREAU_MON       13.50
AMT_REQ_CREDIT_BUREAU_QRT       13.50
AMT_REQ_CREDIT_BUREAU_YEAR      13.50
dtype: float64


null_applicationDF = pd.DataFrame((applicationDF.isnull().sum())*100/applicationDF.shape[0]).reset_index()
null_applicationDF.columns = ['Column Name', 'Null Values Percentage']
fig = plt.figure(figsize=(18,6))
ax = sns.pointplot(x="Column Name",y="Null Values Percentage",data=null_applicationDF,color='blue')
plt.xticks(rotation =90,fontsize =7)
ax.axhline(40, ls='--',color='red')
plt.title("Percentage of Missing values in application data")
plt.ylabel("Null Values PERCENTAGE")
plt.xlabel("COLUMNS")
plt.show()


# more than or equal to 40% empty rows columns
nullcol_40_application = null_applicationDF[null_applicationDF["Null Values Percentage"]>=40]
nullcol_40_application


# How many columns have more than or euqal to 40% null values ?
len(nullcol_40_application)

49


mn.matrix(previousDF)

<Axes: >


# checking the null value % of each column in previousDF dataframe
round(previousDF.isnull().sum() / previousDF.shape[0] * 100.00,2)

SK_ID_PREV                      0.00
SK_ID_CURR                      0.00
NAME_CONTRACT_TYPE              0.00
AMT_ANNUITY                    22.29
AMT_APPLICATION                 0.00
AMT_CREDIT                      0.00
AMT_DOWN_PAYMENT               53.64
AMT_GOODS_PRICE                23.08
WEEKDAY_APPR_PROCESS_START      0.00
HOUR_APPR_PROCESS_START         0.00
FLAG_LAST_APPL_PER_CONTRACT     0.00
NFLAG_LAST_APPL_IN_DAY          0.00
RATE_DOWN_PAYMENT              53.64
RATE_INTEREST_PRIMARY          99.64
RATE_INTEREST_PRIVILEGED       99.64
NAME_CASH_LOAN_PURPOSE          0.00
NAME_CONTRACT_STATUS            0.00
DAYS_DECISION                   0.00
NAME_PAYMENT_TYPE               0.00
CODE_REJECT_REASON              0.00
NAME_TYPE_SUITE                49.12
NAME_CLIENT_TYPE                0.00
NAME_GOODS_CATEGORY             0.00
NAME_PORTFOLIO                  0.00
NAME_PRODUCT_TYPE               0.00
CHANNEL_TYPE                    0.00
SELLERPLACE_AREA                0.00
NAME_SELLER_INDUSTRY            0.00
CNT_PAYMENT                    22.29
NAME_YIELD_GROUP                0.00
PRODUCT_COMBINATION             0.02
DAYS_FIRST_DRAWING             40.30
DAYS_FIRST_DUE                 40.30
DAYS_LAST_DUE_1ST_VERSION      40.30
DAYS_LAST_DUE                  40.30
DAYS_TERMINATION               40.30
NFLAG_INSURED_ON_APPROVAL      40.30
dtype: float64


null_previousDF = pd.DataFrame((previousDF.isnull().sum())*100/previousDF.shape[0]).reset_index()
null_previousDF.columns = ['Column Name', 'Null Values Percentage']
fig = plt.figure(figsize=(18,6))
ax = sns.pointplot(x="Column Name",y="Null Values Percentage",data=null_previousDF,color ='blue')
plt.xticks(rotation =90,fontsize =7)
ax.axhline(40, ls='--',color='red')
plt.title("Percentage of Missing values in previousDF data")
plt.ylabel("Null Values PERCENTAGE")
plt.xlabel("COLUMNS")
plt.show()


# more than or equal to 40% empty rows columns
nullcol_40_previous = null_previousDF[null_previousDF["Null Values Percentage"]>=40]
nullcol_40_previous


# How many columns have more than or euqal to 40% null values ?
len(nullcol_40_previous)

11


# Checking correlation of EXT_SOURCE_X columns vs TARGET column
Source = applicationDF[["EXT_SOURCE_1","EXT_SOURCE_2","EXT_SOURCE_3","TARGET"]]
source_corr = Source.corr()
ax = sns.heatmap(source_corr,
            xticklabels=source_corr.columns,
            yticklabels=source_corr.columns,
            annot = True,
            cmap ="RdYlGn")


# create a list of columns that needs to be dropped including the columns with >40% null values
Unwanted_application = nullcol_40_application["Column Name"].tolist()+ ['EXT_SOURCE_2','EXT_SOURCE_3'] 
# as EXT_SOURCE_1 column is already included in nullcol_40_application 
len(Unwanted_application)

51


# Assuming df_flag is your DataFrame and col_Doc is a list of column names
length = len(col_Doc)
plt.figure(figsize=(20, 25))  # Adjust figsize as needed
for i, j in itertools.zip_longest(col_Doc, range(length)):
    plt.subplot(5, 4, j+1)
    ax = sns.countplot(x=i, hue="TARGET", data=df_flag, palette=["r", "g"])
    plt.yticks(fontsize=8)
    plt.xlabel("")
    plt.title(i)


# Check if 'FLAG_DOCUMENT_3' exists in col_Doc before removing it
if 'FLAG_DOCUMENT_3' in col_Doc:
    col_Doc.remove('FLAG_DOCUMENT_3')

# Add remaining columns in col_Doc to Unwanted_application
Unwanted_application += col_Doc

# Get the length of Unwanted_application
len(Unwanted_application)

70


# checking is there is any correlation between mobile phone, work phone etc, email, Family members and Region rating
contact_col = ['FLAG_MOBIL', 'FLAG_EMP_PHONE', 'FLAG_WORK_PHONE', 'FLAG_CONT_MOBILE',
       'FLAG_PHONE', 'FLAG_EMAIL','TARGET']
Contact_corr = applicationDF[contact_col].corr()
fig = plt.figure(figsize=(8,8))
ax = sns.heatmap(Contact_corr,
            xticklabels=Contact_corr.columns,
            yticklabels=Contact_corr.columns,
            annot = True,
            cmap ="RdYlGn",
            linewidth=1)


# including the 6 FLAG columns to be deleted
contact_col.remove('TARGET') 
Unwanted_application = Unwanted_application + contact_col
len(Unwanted_application)

76


# Dropping the unnecessary columns from applicationDF
applicationDF.drop(labels=Unwanted_application,axis=1,inplace=True)
# Inspecting the dataframe after removal of unnecessary columns
applicationDF.shape

(307511, 46)


# inspecting the column types after removal of unnecessary columns
applicationDF.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Data columns (total 46 columns):
 #   Column                       Non-Null Count   Dtype  
---  ------                       --------------   -----  
 0   SK_ID_CURR                   307511 non-null  int64  
 1   TARGET                       307511 non-null  int64  
 2   NAME_CONTRACT_TYPE           307511 non-null  object 
 3   CODE_GENDER                  307511 non-null  object 
 4   FLAG_OWN_CAR                 307511 non-null  object 
 5   FLAG_OWN_REALTY              307511 non-null  object 
 6   CNT_CHILDREN                 307511 non-null  int64  
 7   AMT_INCOME_TOTAL             307511 non-null  float64
 8   AMT_CREDIT                   307511 non-null  float64
 9   AMT_ANNUITY                  307499 non-null  float64
 10  AMT_GOODS_PRICE              307233 non-null  float64
 11  NAME_TYPE_SUITE              306219 non-null  object 
 12  NAME_INCOME_TYPE             307511 non-null  object 
 13  NAME_EDUCATION_TYPE          307511 non-null  object 
 14  NAME_FAMILY_STATUS           307511 non-null  object 
 15  NAME_HOUSING_TYPE            307511 non-null  object 
 16  REGION_POPULATION_RELATIVE   307511 non-null  float64
 17  DAYS_BIRTH                   307511 non-null  int64  
 18  DAYS_EMPLOYED                307511 non-null  int64  
 19  DAYS_REGISTRATION            307511 non-null  float64
 20  DAYS_ID_PUBLISH              307511 non-null  int64  
 21  OCCUPATION_TYPE              211120 non-null  object 
 22  CNT_FAM_MEMBERS              307509 non-null  float64
 23  REGION_RATING_CLIENT         307511 non-null  int64  
 24  REGION_RATING_CLIENT_W_CITY  307511 non-null  int64  
 25  WEEKDAY_APPR_PROCESS_START   307511 non-null  object 
 26  HOUR_APPR_PROCESS_START      307511 non-null  int64  
 27  REG_REGION_NOT_LIVE_REGION   307511 non-null  int64  
 28  REG_REGION_NOT_WORK_REGION   307511 non-null  int64  
 29  LIVE_REGION_NOT_WORK_REGION  307511 non-null  int64  
 30  REG_CITY_NOT_LIVE_CITY       307511 non-null  int64  
 31  REG_CITY_NOT_WORK_CITY       307511 non-null  int64  
 32  LIVE_CITY_NOT_WORK_CITY      307511 non-null  int64  
 33  ORGANIZATION_TYPE            307511 non-null  object 
 34  OBS_30_CNT_SOCIAL_CIRCLE     306490 non-null  float64
 35  DEF_30_CNT_SOCIAL_CIRCLE     306490 non-null  float64
 36  OBS_60_CNT_SOCIAL_CIRCLE     306490 non-null  float64
 37  DEF_60_CNT_SOCIAL_CIRCLE     306490 non-null  float64
 38  DAYS_LAST_PHONE_CHANGE       307510 non-null  float64
 39  FLAG_DOCUMENT_3              307511 non-null  int64  
 40  AMT_REQ_CREDIT_BUREAU_HOUR   265992 non-null  float64
 41  AMT_REQ_CREDIT_BUREAU_DAY    265992 non-null  float64
 42  AMT_REQ_CREDIT_BUREAU_WEEK   265992 non-null  float64
 43  AMT_REQ_CREDIT_BUREAU_MON    265992 non-null  float64
 44  AMT_REQ_CREDIT_BUREAU_QRT    265992 non-null  float64
 45  AMT_REQ_CREDIT_BUREAU_YEAR   265992 non-null  float64
dtypes: float64(18), int64(16), object(12)
memory usage: 107.9+ MB


# Getting the 11 columns which has more than 40% unknown
Unwanted_previous = nullcol_40_previous["Column Name"].tolist()
Unwanted_previous

['AMT_DOWN_PAYMENT',
 'RATE_DOWN_PAYMENT',
 'RATE_INTEREST_PRIMARY',
 'RATE_INTEREST_PRIVILEGED',
 'NAME_TYPE_SUITE',
 'DAYS_FIRST_DRAWING',
 'DAYS_FIRST_DUE',
 'DAYS_LAST_DUE_1ST_VERSION',
 'DAYS_LAST_DUE',
 'DAYS_TERMINATION',
 'NFLAG_INSURED_ON_APPROVAL']


# Listing down columns which are not needed
Unnecessary_previous = ['WEEKDAY_APPR_PROCESS_START','HOUR_APPR_PROCESS_START',
                        'FLAG_LAST_APPL_PER_CONTRACT','NFLAG_LAST_APPL_IN_DAY']
Unwanted_previous = Unwanted_previous + Unnecessary_previous
len(Unwanted_previous)

15


# Dropping the unnecessary columns from previous
previousDF.drop(labels=Unwanted_previous,axis=1,inplace=True)
# Inspecting the dataframe after removal of unnecessary columns
previousDF.shape

(1670214, 22)


# inspecting the column types after after removal of unnecessary columns
previousDF.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1670214 entries, 0 to 1670213
Data columns (total 22 columns):
 #   Column                  Non-Null Count    Dtype  
---  ------                  --------------    -----  
 0   SK_ID_PREV              1670214 non-null  int64  
 1   SK_ID_CURR              1670214 non-null  int64  
 2   NAME_CONTRACT_TYPE      1670214 non-null  object 
 3   AMT_ANNUITY             1297979 non-null  float64
 4   AMT_APPLICATION         1670214 non-null  float64
 5   AMT_CREDIT              1670213 non-null  float64
 6   AMT_GOODS_PRICE         1284699 non-null  float64
 7   NAME_CASH_LOAN_PURPOSE  1670214 non-null  object 
 8   NAME_CONTRACT_STATUS    1670214 non-null  object 
 9   DAYS_DECISION           1670214 non-null  int64  
 10  NAME_PAYMENT_TYPE       1670214 non-null  object 
 11  CODE_REJECT_REASON      1670214 non-null  object 
 12  NAME_CLIENT_TYPE        1670214 non-null  object 
 13  NAME_GOODS_CATEGORY     1670214 non-null  object 
 14  NAME_PORTFOLIO          1670214 non-null  object 
 15  NAME_PRODUCT_TYPE       1670214 non-null  object 
 16  CHANNEL_TYPE            1670214 non-null  object 
 17  SELLERPLACE_AREA        1670214 non-null  int64  
 18  NAME_SELLER_INDUSTRY    1670214 non-null  object 
 19  CNT_PAYMENT             1297984 non-null  float64
 20  NAME_YIELD_GROUP        1670214 non-null  object 
 21  PRODUCT_COMBINATION     1669868 non-null  object 
dtypes: float64(5), int64(4), object(13)
memory usage: 280.3+ MB


# Converting Negative days to positive days

date_col = ['DAYS_BIRTH','DAYS_EMPLOYED','DAYS_REGISTRATION','DAYS_ID_PUBLISH']

for col in date_col:
    applicationDF[col] = abs(applicationDF[col])
# Binning Numerical Columns to create a categorical column

# Creating bins for income amount
applicationDF['AMT_INCOME_TOTAL']=applicationDF['AMT_INCOME_TOTAL']/100000

bins = [0,1,2,3,4,5,6,7,8,9,10,11]
slot = ['0-100K','100K-200K', '200k-300k','300k-400k','400k-500k','500k-600k','600k-700k','700k-800k','800k-900k','900k-1M', '1M Above']

applicationDF['AMT_INCOME_RANGE']=pd.cut(applicationDF['AMT_INCOME_TOTAL'],bins,labels=slot)


applicationDF['AMT_INCOME_RANGE'].value_counts(normalize=True)*100

AMT_INCOME_RANGE
100K-200K    50.735000
200k-300k    21.210691
0-100K       20.729695
300k-400k     4.776116
400k-500k     1.744669
500k-600k     0.356354
600k-700k     0.282805
800k-900k     0.096980
700k-800k     0.052721
900k-1M       0.009112
1M Above      0.005858
Name: proportion, dtype: float64


# Creating bins for Credit amount
applicationDF['AMT_CREDIT']=applicationDF['AMT_CREDIT']/100000

bins = [0,1,2,3,4,5,6,7,8,9,10,100]
slots = ['0-100K','100K-200K', '200k-300k','300k-400k','400k-500k','500k-600k','600k-700k','700k-800k',
       '800k-900k','900k-1M', '1M Above']

applicationDF['AMT_CREDIT_RANGE']=pd.cut(applicationDF['AMT_CREDIT'],bins=bins,labels=slots)


#checking the binning of data and % of data in each category
applicationDF['AMT_CREDIT_RANGE'].value_counts(normalize=True)*100

AMT_CREDIT_RANGE
200k-300k    17.824728
1M Above     16.254703
500k-600k    11.131960
400k-500k    10.418489
100K-200K     9.801275
300k-400k     8.564897
600k-700k     7.820533
800k-900k     7.086576
700k-800k     6.241403
900k-1M       2.902986
0-100K        1.952450
Name: proportion, dtype: float64


# Creating bins for Age
applicationDF['AGE'] = applicationDF['DAYS_BIRTH'] // 365
bins = [0,20,30,40,50,100]
slots = ['0-20','20-30','30-40','40-50','50 above']

applicationDF['AGE_GROUP']=pd.cut(applicationDF['AGE'],bins=bins,labels=slots)


#checking the binning of data and % of data in each category
applicationDF['AGE_GROUP'].value_counts(normalize=True)*100

AGE_GROUP
50 above    31.604398
30-40       27.028952
40-50       24.194582
20-30       17.171743
0-20         0.000325
Name: proportion, dtype: float64


# Creating bins for Employement Time
applicationDF['YEARS_EMPLOYED'] = applicationDF['DAYS_EMPLOYED'] // 365
bins = [0,5,10,20,30,40,50,60,150]
slots = ['0-5','5-10','10-20','20-30','30-40','40-50','50-60','60 above']

applicationDF['EMPLOYMENT_YEAR']=pd.cut(applicationDF['YEARS_EMPLOYED'],bins=bins,labels=slots)


#checking the binning of data and % of data in each category
applicationDF['EMPLOYMENT_YEAR'].value_counts(normalize=True)*100

EMPLOYMENT_YEAR
0-5         55.582363
5-10        24.966441
10-20       14.564315
20-30        3.750117
30-40        1.058720
40-50        0.078044
50-60        0.000000
60 above     0.000000
Name: proportion, dtype: float64


#Checking the number of unique values each column possess to identify categorical columns
applicationDF.nunique().sort_values()

LIVE_CITY_NOT_WORK_CITY             2
TARGET                              2
NAME_CONTRACT_TYPE                  2
REG_REGION_NOT_LIVE_REGION          2
FLAG_OWN_CAR                        2
FLAG_OWN_REALTY                     2
REG_REGION_NOT_WORK_REGION          2
LIVE_REGION_NOT_WORK_REGION         2
FLAG_DOCUMENT_3                     2
REG_CITY_NOT_LIVE_CITY              2
REG_CITY_NOT_WORK_CITY              2
REGION_RATING_CLIENT                3
CODE_GENDER                         3
REGION_RATING_CLIENT_W_CITY         3
AMT_REQ_CREDIT_BUREAU_HOUR          5
NAME_EDUCATION_TYPE                 5
AGE_GROUP                           5
NAME_FAMILY_STATUS                  6
NAME_HOUSING_TYPE                   6
EMPLOYMENT_YEAR                     6
WEEKDAY_APPR_PROCESS_START          7
NAME_TYPE_SUITE                     7
NAME_INCOME_TYPE                    8
AMT_REQ_CREDIT_BUREAU_WEEK          9
AMT_REQ_CREDIT_BUREAU_DAY           9
DEF_60_CNT_SOCIAL_CIRCLE            9
DEF_30_CNT_SOCIAL_CIRCLE           10
AMT_CREDIT_RANGE                   11
AMT_INCOME_RANGE                   11
AMT_REQ_CREDIT_BUREAU_QRT          11
CNT_CHILDREN                       15
CNT_FAM_MEMBERS                    17
OCCUPATION_TYPE                    18
HOUR_APPR_PROCESS_START            24
AMT_REQ_CREDIT_BUREAU_MON          24
AMT_REQ_CREDIT_BUREAU_YEAR         25
OBS_60_CNT_SOCIAL_CIRCLE           33
OBS_30_CNT_SOCIAL_CIRCLE           33
AGE                                50
YEARS_EMPLOYED                     51
ORGANIZATION_TYPE                  58
REGION_POPULATION_RELATIVE         81
AMT_GOODS_PRICE                  1002
AMT_INCOME_TOTAL                 2548
DAYS_LAST_PHONE_CHANGE           3773
AMT_CREDIT                       5603
DAYS_ID_PUBLISH                  6168
DAYS_EMPLOYED                   12574
AMT_ANNUITY                     13672
DAYS_REGISTRATION               15688
DAYS_BIRTH                      17460
SK_ID_CURR                     307511
dtype: int64


# inspecting the column types if they are in correct data type using the above result.
applicationDF.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Data columns (total 52 columns):
 #   Column                       Non-Null Count   Dtype   
---  ------                       --------------   -----   
 0   SK_ID_CURR                   307511 non-null  int64   
 1   TARGET                       307511 non-null  int64   
 2   NAME_CONTRACT_TYPE           307511 non-null  category
 3   CODE_GENDER                  307511 non-null  category
 4   FLAG_OWN_CAR                 307511 non-null  category
 5   FLAG_OWN_REALTY              307511 non-null  category
 6   CNT_CHILDREN                 307511 non-null  int64   
 7   AMT_INCOME_TOTAL             307511 non-null  float64 
 8   AMT_CREDIT                   307511 non-null  float64 
 9   AMT_ANNUITY                  307499 non-null  float64 
 10  AMT_GOODS_PRICE              307233 non-null  float64 
 11  NAME_TYPE_SUITE              307511 non-null  category
 12  NAME_INCOME_TYPE             307511 non-null  category
 13  NAME_EDUCATION_TYPE          307511 non-null  category
 14  NAME_FAMILY_STATUS           307511 non-null  category
 15  NAME_HOUSING_TYPE            307511 non-null  category
 16  REGION_POPULATION_RELATIVE   307511 non-null  float64 
 17  DAYS_BIRTH                   307511 non-null  int64   
 18  DAYS_EMPLOYED                307511 non-null  int64   
 19  DAYS_REGISTRATION            307511 non-null  float64 
 20  DAYS_ID_PUBLISH              307511 non-null  int64   
 21  OCCUPATION_TYPE              307511 non-null  category
 22  CNT_FAM_MEMBERS              307509 non-null  float64 
 23  REGION_RATING_CLIENT         307511 non-null  category
 24  REGION_RATING_CLIENT_W_CITY  307511 non-null  category
 25  WEEKDAY_APPR_PROCESS_START   307511 non-null  category
 26  HOUR_APPR_PROCESS_START      307511 non-null  int64   
 27  REG_REGION_NOT_LIVE_REGION   307511 non-null  int64   
 28  REG_REGION_NOT_WORK_REGION   307511 non-null  category
 29  LIVE_REGION_NOT_WORK_REGION  307511 non-null  category
 30  REG_CITY_NOT_LIVE_CITY       307511 non-null  category
 31  REG_CITY_NOT_WORK_CITY       307511 non-null  category
 32  LIVE_CITY_NOT_WORK_CITY      307511 non-null  category
 33  ORGANIZATION_TYPE            307511 non-null  category
 34  OBS_30_CNT_SOCIAL_CIRCLE     306490 non-null  float64 
 35  DEF_30_CNT_SOCIAL_CIRCLE     306490 non-null  float64 
 36  OBS_60_CNT_SOCIAL_CIRCLE     306490 non-null  float64 
 37  DEF_60_CNT_SOCIAL_CIRCLE     306490 non-null  float64 
 38  DAYS_LAST_PHONE_CHANGE       307510 non-null  float64 
 39  FLAG_DOCUMENT_3              307511 non-null  int64   
 40  AMT_REQ_CREDIT_BUREAU_HOUR   307511 non-null  float64 
 41  AMT_REQ_CREDIT_BUREAU_DAY    307511 non-null  float64 
 42  AMT_REQ_CREDIT_BUREAU_WEEK   307511 non-null  float64 
 43  AMT_REQ_CREDIT_BUREAU_MON    307511 non-null  float64 
 44  AMT_REQ_CREDIT_BUREAU_QRT    307511 non-null  float64 
 45  AMT_REQ_CREDIT_BUREAU_YEAR   307511 non-null  float64 
 46  AMT_INCOME_RANGE             307279 non-null  category
 47  AMT_CREDIT_RANGE             307511 non-null  category
 48  AGE                          307511 non-null  int64   
 49  AGE_GROUP                    307511 non-null  category
 50  YEARS_EMPLOYED               307511 non-null  int64   
 51  EMPLOYMENT_YEAR              224233 non-null  category
dtypes: category(23), float64(18), int64(11)
memory usage: 74.8 MB


#Conversion of Object and Numerical columns to Categorical Columns
categorical_columns = ['NAME_CONTRACT_TYPE','CODE_GENDER','NAME_TYPE_SUITE','NAME_INCOME_TYPE','NAME_EDUCATION_TYPE',
                       'NAME_FAMILY_STATUS','NAME_HOUSING_TYPE','OCCUPATION_TYPE','WEEKDAY_APPR_PROCESS_START',
                       'ORGANIZATION_TYPE','FLAG_OWN_CAR','FLAG_OWN_REALTY','LIVE_CITY_NOT_WORK_CITY',
                       'REG_CITY_NOT_LIVE_CITY','REG_CITY_NOT_WORK_CITY','REG_REGION_NOT_WORK_REGION',
                       'LIVE_REGION_NOT_WORK_REGION','REGION_RATING_CLIENT','WEEKDAY_APPR_PROCESS_START',
                       'REGION_RATING_CLIENT_W_CITY'
                      ]
for col in categorical_columns:
    applicationDF[col] =pd.Categorical(applicationDF[col])


# inspecting the column types if the above conversion is reflected
applicationDF.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Data columns (total 52 columns):
 #   Column                       Non-Null Count   Dtype   
---  ------                       --------------   -----   
 0   SK_ID_CURR                   307511 non-null  int64   
 1   TARGET                       307511 non-null  int64   
 2   NAME_CONTRACT_TYPE           307511 non-null  category
 3   CODE_GENDER                  307511 non-null  category
 4   FLAG_OWN_CAR                 307511 non-null  category
 5   FLAG_OWN_REALTY              307511 non-null  category
 6   CNT_CHILDREN                 307511 non-null  int64   
 7   AMT_INCOME_TOTAL             307511 non-null  float64 
 8   AMT_CREDIT                   307511 non-null  float64 
 9   AMT_ANNUITY                  307499 non-null  float64 
 10  AMT_GOODS_PRICE              307233 non-null  float64 
 11  NAME_TYPE_SUITE              306219 non-null  category
 12  NAME_INCOME_TYPE             307511 non-null  category
 13  NAME_EDUCATION_TYPE          307511 non-null  category
 14  NAME_FAMILY_STATUS           307511 non-null  category
 15  NAME_HOUSING_TYPE            307511 non-null  category
 16  REGION_POPULATION_RELATIVE   307511 non-null  float64 
 17  DAYS_BIRTH                   307511 non-null  int64   
 18  DAYS_EMPLOYED                307511 non-null  int64   
 19  DAYS_REGISTRATION            307511 non-null  float64 
 20  DAYS_ID_PUBLISH              307511 non-null  int64   
 21  OCCUPATION_TYPE              211120 non-null  category
 22  CNT_FAM_MEMBERS              307509 non-null  float64 
 23  REGION_RATING_CLIENT         307511 non-null  category
 24  REGION_RATING_CLIENT_W_CITY  307511 non-null  category
 25  WEEKDAY_APPR_PROCESS_START   307511 non-null  category
 26  HOUR_APPR_PROCESS_START      307511 non-null  int64   
 27  REG_REGION_NOT_LIVE_REGION   307511 non-null  int64   
 28  REG_REGION_NOT_WORK_REGION   307511 non-null  category
 29  LIVE_REGION_NOT_WORK_REGION  307511 non-null  category
 30  REG_CITY_NOT_LIVE_CITY       307511 non-null  category
 31  REG_CITY_NOT_WORK_CITY       307511 non-null  category
 32  LIVE_CITY_NOT_WORK_CITY      307511 non-null  category
 33  ORGANIZATION_TYPE            307511 non-null  category
 34  OBS_30_CNT_SOCIAL_CIRCLE     306490 non-null  float64 
 35  DEF_30_CNT_SOCIAL_CIRCLE     306490 non-null  float64 
 36  OBS_60_CNT_SOCIAL_CIRCLE     306490 non-null  float64 
 37  DEF_60_CNT_SOCIAL_CIRCLE     306490 non-null  float64 
 38  DAYS_LAST_PHONE_CHANGE       307510 non-null  float64 
 39  FLAG_DOCUMENT_3              307511 non-null  int64   
 40  AMT_REQ_CREDIT_BUREAU_HOUR   265992 non-null  float64 
 41  AMT_REQ_CREDIT_BUREAU_DAY    265992 non-null  float64 
 42  AMT_REQ_CREDIT_BUREAU_WEEK   265992 non-null  float64 
 43  AMT_REQ_CREDIT_BUREAU_MON    265992 non-null  float64 
 44  AMT_REQ_CREDIT_BUREAU_QRT    265992 non-null  float64 
 45  AMT_REQ_CREDIT_BUREAU_YEAR   265992 non-null  float64 
 46  AMT_INCOME_RANGE             307279 non-null  category
 47  AMT_CREDIT_RANGE             307511 non-null  category
 48  AGE                          307511 non-null  int64   
 49  AGE_GROUP                    307511 non-null  category
 50  YEARS_EMPLOYED               307511 non-null  int64   
 51  EMPLOYMENT_YEAR              224233 non-null  category
dtypes: category(23), float64(18), int64(11)
memory usage: 74.8 MB


#Checking the number of unique values each column possess to identify categorical columns
previousDF.nunique().sort_values()

NAME_PRODUCT_TYPE               3
NAME_PAYMENT_TYPE               4
NAME_CONTRACT_TYPE              4
NAME_CLIENT_TYPE                4
NAME_CONTRACT_STATUS            4
NAME_PORTFOLIO                  5
NAME_YIELD_GROUP                5
CHANNEL_TYPE                    8
CODE_REJECT_REASON              9
NAME_SELLER_INDUSTRY           11
PRODUCT_COMBINATION            17
NAME_CASH_LOAN_PURPOSE         25
NAME_GOODS_CATEGORY            28
CNT_PAYMENT                    49
SELLERPLACE_AREA             2097
DAYS_DECISION                2922
AMT_CREDIT                  86803
AMT_GOODS_PRICE             93885
AMT_APPLICATION             93885
SK_ID_CURR                 338857
AMT_ANNUITY                357959
SK_ID_PREV                1670214
dtype: int64


# inspecting the column types if the above conversion is reflected
previousDF.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1670214 entries, 0 to 1670213
Data columns (total 22 columns):
 #   Column                  Non-Null Count    Dtype  
---  ------                  --------------    -----  
 0   SK_ID_PREV              1670214 non-null  int64  
 1   SK_ID_CURR              1670214 non-null  int64  
 2   NAME_CONTRACT_TYPE      1670214 non-null  object 
 3   AMT_ANNUITY             1297979 non-null  float64
 4   AMT_APPLICATION         1670214 non-null  float64
 5   AMT_CREDIT              1670213 non-null  float64
 6   AMT_GOODS_PRICE         1284699 non-null  float64
 7   NAME_CASH_LOAN_PURPOSE  1670214 non-null  object 
 8   NAME_CONTRACT_STATUS    1670214 non-null  object 
 9   DAYS_DECISION           1670214 non-null  int64  
 10  NAME_PAYMENT_TYPE       1670214 non-null  object 
 11  CODE_REJECT_REASON      1670214 non-null  object 
 12  NAME_CLIENT_TYPE        1670214 non-null  object 
 13  NAME_GOODS_CATEGORY     1670214 non-null  object 
 14  NAME_PORTFOLIO          1670214 non-null  object 
 15  NAME_PRODUCT_TYPE       1670214 non-null  object 
 16  CHANNEL_TYPE            1670214 non-null  object 
 17  SELLERPLACE_AREA        1670214 non-null  int64  
 18  NAME_SELLER_INDUSTRY    1670214 non-null  object 
 19  CNT_PAYMENT             1297984 non-null  float64
 20  NAME_YIELD_GROUP        1670214 non-null  object 
 21  PRODUCT_COMBINATION     1669868 non-null  object 
dtypes: float64(5), int64(4), object(13)
memory usage: 280.3+ MB


#Converting negative days to positive days 
previousDF['DAYS_DECISION'] = abs(previousDF['DAYS_DECISION'])
#age group calculation e.g. 388 will be grouped as 300-400
previousDF['DAYS_DECISION_GROUP'] = (previousDF['DAYS_DECISION']-(previousDF['DAYS_DECISION'] % 400)).astype(str)+'-'+ ((previousDF['DAYS_DECISION'] - (previousDF['DAYS_DECISION'] % 400)) + (previousDF['DAYS_DECISION'] % 400) + (400 - (previousDF['DAYS_DECISION'] % 400))).astype(str)


previousDF['DAYS_DECISION_GROUP'].value_counts(normalize=True)*100

DAYS_DECISION_GROUP
0-400        37.490525
400-800      22.944724
800-1200     12.444753
1200-1600     7.904556
2400-2800     6.297456
1600-2000     5.795784
2000-2400     5.684960
2800-3200     1.437241
Name: proportion, dtype: float64


#Converting Categorical columns from Object to categorical 
Catgorical_col_p = ['NAME_CASH_LOAN_PURPOSE','NAME_CONTRACT_STATUS','NAME_PAYMENT_TYPE',
                    'CODE_REJECT_REASON','NAME_CLIENT_TYPE','NAME_GOODS_CATEGORY','NAME_PORTFOLIO',
                   'NAME_PRODUCT_TYPE','CHANNEL_TYPE','NAME_SELLER_INDUSTRY','NAME_YIELD_GROUP','PRODUCT_COMBINATION',
                    'NAME_CONTRACT_TYPE','DAYS_DECISION_GROUP']

for col in Catgorical_col_p:
    previousDF[col] =pd.Categorical(previousDF[col])


# inspecting the column types after conversion
previousDF.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1670214 entries, 0 to 1670213
Data columns (total 23 columns):
 #   Column                  Non-Null Count    Dtype   
---  ------                  --------------    -----   
 0   SK_ID_PREV              1670214 non-null  int64   
 1   SK_ID_CURR              1670214 non-null  int64   
 2   NAME_CONTRACT_TYPE      1670214 non-null  category
 3   AMT_ANNUITY             1670214 non-null  float64 
 4   AMT_APPLICATION         1670214 non-null  float64 
 5   AMT_CREDIT              1670213 non-null  float64 
 6   AMT_GOODS_PRICE         1670214 non-null  float64 
 7   NAME_CASH_LOAN_PURPOSE  1670214 non-null  category
 8   NAME_CONTRACT_STATUS    1670214 non-null  category
 9   DAYS_DECISION           1670214 non-null  int64   
 10  NAME_PAYMENT_TYPE       1670214 non-null  category
 11  CODE_REJECT_REASON      1670214 non-null  category
 12  NAME_CLIENT_TYPE        1670214 non-null  category
 13  NAME_GOODS_CATEGORY     1670214 non-null  category
 14  NAME_PORTFOLIO          1670214 non-null  category
 15  NAME_PRODUCT_TYPE       1670214 non-null  category
 16  CHANNEL_TYPE            1670214 non-null  category
 17  SELLERPLACE_AREA        1670214 non-null  int64   
 18  NAME_SELLER_INDUSTRY    1670214 non-null  category
 19  CNT_PAYMENT             1297984 non-null  float64 
 20  NAME_YIELD_GROUP        1670214 non-null  category
 21  PRODUCT_COMBINATION     1669868 non-null  category
 22  DAYS_DECISION_GROUP     1670214 non-null  category
dtypes: category(14), float64(5), int64(4)
memory usage: 137.0 MB


#Imputing Null Values in applicationDF
# checking the null value % of each column in applicationDF dataframe
round(applicationDF.isnull().sum() / applicationDF.shape[0] * 100.00,2)

SK_ID_CURR                      0.00
TARGET                          0.00
NAME_CONTRACT_TYPE              0.00
CODE_GENDER                     0.00
FLAG_OWN_CAR                    0.00
FLAG_OWN_REALTY                 0.00
CNT_CHILDREN                    0.00
AMT_INCOME_TOTAL                0.00
AMT_CREDIT                      0.00
AMT_ANNUITY                     0.00
AMT_GOODS_PRICE                 0.09
NAME_TYPE_SUITE                 0.00
NAME_INCOME_TYPE                0.00
NAME_EDUCATION_TYPE             0.00
NAME_FAMILY_STATUS              0.00
NAME_HOUSING_TYPE               0.00
REGION_POPULATION_RELATIVE      0.00
DAYS_BIRTH                      0.00
DAYS_EMPLOYED                   0.00
DAYS_REGISTRATION               0.00
DAYS_ID_PUBLISH                 0.00
OCCUPATION_TYPE                 0.00
CNT_FAM_MEMBERS                 0.00
REGION_RATING_CLIENT            0.00
REGION_RATING_CLIENT_W_CITY     0.00
WEEKDAY_APPR_PROCESS_START      0.00
HOUR_APPR_PROCESS_START         0.00
REG_REGION_NOT_LIVE_REGION      0.00
REG_REGION_NOT_WORK_REGION      0.00
LIVE_REGION_NOT_WORK_REGION     0.00
REG_CITY_NOT_LIVE_CITY          0.00
REG_CITY_NOT_WORK_CITY          0.00
LIVE_CITY_NOT_WORK_CITY         0.00
ORGANIZATION_TYPE               0.00
OBS_30_CNT_SOCIAL_CIRCLE        0.33
DEF_30_CNT_SOCIAL_CIRCLE        0.33
OBS_60_CNT_SOCIAL_CIRCLE        0.33
DEF_60_CNT_SOCIAL_CIRCLE        0.33
DAYS_LAST_PHONE_CHANGE          0.00
FLAG_DOCUMENT_3                 0.00
AMT_REQ_CREDIT_BUREAU_HOUR      0.00
AMT_REQ_CREDIT_BUREAU_DAY       0.00
AMT_REQ_CREDIT_BUREAU_WEEK      0.00
AMT_REQ_CREDIT_BUREAU_MON       0.00
AMT_REQ_CREDIT_BUREAU_QRT       0.00
AMT_REQ_CREDIT_BUREAU_YEAR      0.00
AMT_INCOME_RANGE                0.08
AMT_CREDIT_RANGE                0.00
AGE                             0.00
AGE_GROUP                       0.00
YEARS_EMPLOYED                  0.00
EMPLOYMENT_YEAR                27.08
dtype: float64


applicationDF['NAME_TYPE_SUITE'].describe()

count            306219
unique                7
top       Unaccompanied
freq             248526
Name: NAME_TYPE_SUITE, dtype: object


applicationDF['NAME_TYPE_SUITE'].fillna((applicationDF['NAME_TYPE_SUITE'].mode()[0]),inplace = True)


applicationDF['OCCUPATION_TYPE'] = applicationDF['OCCUPATION_TYPE'].cat.add_categories('Unknown')
applicationDF['OCCUPATION_TYPE'].fillna('Unknown', inplace =True)


applicationDF[['AMT_REQ_CREDIT_BUREAU_HOUR','AMT_REQ_CREDIT_BUREAU_DAY',
               'AMT_REQ_CREDIT_BUREAU_WEEK','AMT_REQ_CREDIT_BUREAU_MON',
               'AMT_REQ_CREDIT_BUREAU_QRT','AMT_REQ_CREDIT_BUREAU_YEAR']].describe()


amount = ['AMT_REQ_CREDIT_BUREAU_HOUR', 'AMT_REQ_CREDIT_BUREAU_DAY','AMT_REQ_CREDIT_BUREAU_WEEK','AMT_REQ_CREDIT_BUREAU_MON',
         'AMT_REQ_CREDIT_BUREAU_QRT','AMT_REQ_CREDIT_BUREAU_YEAR']

for col in amount:
    applicationDF[col].fillna(applicationDF[col].median(),inplace = True)


# checking the null value % of each column in previousDF dataframe
round(applicationDF.isnull().sum() / previousDF.shape[0] * 100.00,2)

SK_ID_CURR                     0.00
TARGET                         0.00
NAME_CONTRACT_TYPE             0.00
CODE_GENDER                    0.00
FLAG_OWN_CAR                   0.00
FLAG_OWN_REALTY                0.00
CNT_CHILDREN                   0.00
AMT_INCOME_TOTAL               0.00
AMT_CREDIT                     0.00
AMT_ANNUITY                    0.00
AMT_GOODS_PRICE                0.02
NAME_TYPE_SUITE                0.00
NAME_INCOME_TYPE               0.00
NAME_EDUCATION_TYPE            0.00
NAME_FAMILY_STATUS             0.00
NAME_HOUSING_TYPE              0.00
REGION_POPULATION_RELATIVE     0.00
DAYS_BIRTH                     0.00
DAYS_EMPLOYED                  0.00
DAYS_REGISTRATION              0.00
DAYS_ID_PUBLISH                0.00
OCCUPATION_TYPE                0.00
CNT_FAM_MEMBERS                0.00
REGION_RATING_CLIENT           0.00
REGION_RATING_CLIENT_W_CITY    0.00
WEEKDAY_APPR_PROCESS_START     0.00
HOUR_APPR_PROCESS_START        0.00
REG_REGION_NOT_LIVE_REGION     0.00
REG_REGION_NOT_WORK_REGION     0.00
LIVE_REGION_NOT_WORK_REGION    0.00
REG_CITY_NOT_LIVE_CITY         0.00
REG_CITY_NOT_WORK_CITY         0.00
LIVE_CITY_NOT_WORK_CITY        0.00
ORGANIZATION_TYPE              0.00
OBS_30_CNT_SOCIAL_CIRCLE       0.06
DEF_30_CNT_SOCIAL_CIRCLE       0.06
OBS_60_CNT_SOCIAL_CIRCLE       0.06
DEF_60_CNT_SOCIAL_CIRCLE       0.06
DAYS_LAST_PHONE_CHANGE         0.00
FLAG_DOCUMENT_3                0.00
AMT_REQ_CREDIT_BUREAU_HOUR     0.00
AMT_REQ_CREDIT_BUREAU_DAY      0.00
AMT_REQ_CREDIT_BUREAU_WEEK     0.00
AMT_REQ_CREDIT_BUREAU_MON      0.00
AMT_REQ_CREDIT_BUREAU_QRT      0.00
AMT_REQ_CREDIT_BUREAU_YEAR     0.00
AMT_INCOME_RANGE               0.01
AMT_CREDIT_RANGE               0.00
AGE                            0.00
AGE_GROUP                      0.00
YEARS_EMPLOYED                 0.00
EMPLOYMENT_YEAR                4.99
dtype: float64


# checking the null value % of each column in previousDF dataframe
round(previousDF.isnull().sum() / previousDF.shape[0] * 100.00,2)

SK_ID_PREV                 0.00
SK_ID_CURR                 0.00
NAME_CONTRACT_TYPE         0.00
AMT_ANNUITY               22.29
AMT_APPLICATION            0.00
AMT_CREDIT                 0.00
AMT_GOODS_PRICE           23.08
NAME_CASH_LOAN_PURPOSE     0.00
NAME_CONTRACT_STATUS       0.00
DAYS_DECISION              0.00
NAME_PAYMENT_TYPE          0.00
CODE_REJECT_REASON         0.00
NAME_CLIENT_TYPE           0.00
NAME_GOODS_CATEGORY        0.00
NAME_PORTFOLIO             0.00
NAME_PRODUCT_TYPE          0.00
CHANNEL_TYPE               0.00
SELLERPLACE_AREA           0.00
NAME_SELLER_INDUSTRY       0.00
CNT_PAYMENT               22.29
NAME_YIELD_GROUP           0.00
PRODUCT_COMBINATION        0.02
DAYS_DECISION_GROUP        0.00
dtype: float64


plt.figure(figsize=(4,4))
sns.kdeplot(previousDF['AMT_ANNUITY'])
plt.show()


previousDF['AMT_ANNUITY'].fillna(previousDF['AMT_ANNUITY'].median(),inplace = True)


plt.figure(figsize=(3,3))
sns.kdeplot(previousDF['AMT_GOODS_PRICE'][pd.notnull(previousDF['AMT_GOODS_PRICE'])])
plt.show()


statsDF = pd.DataFrame() # new dataframe with columns imputed with mode, median and mean
statsDF['AMT_GOODS_PRICE_mode'] = previousDF['AMT_GOODS_PRICE'].fillna(previousDF['AMT_GOODS_PRICE'].mode()[0])
statsDF['AMT_GOODS_PRICE_median'] = previousDF['AMT_GOODS_PRICE'].fillna(previousDF['AMT_GOODS_PRICE'].median())
statsDF['AMT_GOODS_PRICE_mean'] = previousDF['AMT_GOODS_PRICE'].fillna(previousDF['AMT_GOODS_PRICE'].mean())

cols = ['AMT_GOODS_PRICE_mode', 'AMT_GOODS_PRICE_median','AMT_GOODS_PRICE_mean']

plt.figure(figsize=(18,10))
plt.suptitle('Distribution of Original data vs imputed data')
plt.subplot(221)
sns.distplot(previousDF['AMT_GOODS_PRICE'][pd.notnull(previousDF['AMT_GOODS_PRICE'])]);
for i in enumerate(cols): 
    plt.subplot(2,2,i[0]+2)
    sns.distplot(statsDF[i[1]])


previousDF['AMT_GOODS_PRICE'].fillna(previousDF['AMT_GOODS_PRICE'].mode()[0], inplace=True)


# checking the null value % of each column in previousDF dataframe
round(previousDF.isnull().sum() / previousDF.shape[0] * 100.00,2)

SK_ID_PREV                 0.00
SK_ID_CURR                 0.00
NAME_CONTRACT_TYPE         0.00
AMT_ANNUITY                0.00
AMT_APPLICATION            0.00
AMT_CREDIT                 0.00
AMT_GOODS_PRICE            0.00
NAME_CASH_LOAN_PURPOSE     0.00
NAME_CONTRACT_STATUS       0.00
DAYS_DECISION              0.00
NAME_PAYMENT_TYPE          0.00
CODE_REJECT_REASON         0.00
NAME_CLIENT_TYPE           0.00
NAME_GOODS_CATEGORY        0.00
NAME_PORTFOLIO             0.00
NAME_PRODUCT_TYPE          0.00
CHANNEL_TYPE               0.00
SELLERPLACE_AREA           0.00
NAME_SELLER_INDUSTRY       0.00
CNT_PAYMENT               22.29
NAME_YIELD_GROUP           0.00
PRODUCT_COMBINATION        0.02
DAYS_DECISION_GROUP        0.00
dtype: float64


plt.figure(figsize=(22,10))

app_outlier_col_1 = ['AMT_ANNUITY','AMT_INCOME_TOTAL','AMT_CREDIT','AMT_GOODS_PRICE','DAYS_EMPLOYED']
app_outlier_col_2 = ['CNT_CHILDREN','DAYS_BIRTH']
for i in enumerate(app_outlier_col_1):
    plt.subplot(2,4,i[0]+1)
    sns.boxplot(y=applicationDF[i[1]])
    plt.title(i[1])
    plt.ylabel("")

for i in enumerate(app_outlier_col_2):
    plt.subplot(2,4,i[0]+6)
    sns.boxplot(y=applicationDF[i[1]])
    plt.title(i[1])
    plt.ylabel("")


applicationDF[['AMT_ANNUITY', 'AMT_INCOME_TOTAL', 'AMT_CREDIT', 'AMT_GOODS_PRICE', 'DAYS_BIRTH','CNT_CHILDREN','DAYS_EMPLOYED']].describe()


plt.figure(figsize=(22,8))

prev_outlier_col_1 = ['AMT_ANNUITY','AMT_APPLICATION','AMT_CREDIT','AMT_GOODS_PRICE','SELLERPLACE_AREA']
prev_outlier_col_2 = ['SK_ID_CURR','DAYS_DECISION','CNT_PAYMENT']
for i in enumerate(prev_outlier_col_1):
    plt.subplot(2,4,i[0]+1)
    sns.boxplot(y=previousDF[i[1]])
    plt.title(i[1])
    plt.ylabel("")

for i in enumerate(prev_outlier_col_2):
    plt.subplot(2,4,i[0]+6)
    sns.boxplot(y=previousDF[i[1]])
    plt.title(i[1])
    plt.ylabel("")


previousDF[['AMT_ANNUITY', 'AMT_APPLICATION', 'AMT_CREDIT', 'AMT_GOODS_PRICE', 'SELLERPLACE_AREA','CNT_PAYMENT','DAYS_DECISION']].describe()


Imbalance = applicationDF["TARGET"].value_counts().reset_index()

plt.figure(figsize=(10, 4))
x = ['Repayer', 'Defaulter']
sns.barplot(x=x, y="TARGET", data=Imbalance, palette=['g', 'r'])
plt.xlabel("Loan Repayment Status")
plt.ylabel("Count of Repayers & Defaulters")
plt.title("Imbalance Plotting")
plt.show()


count_0 = Imbalance.iloc[0]["TARGET"]
count_1 = Imbalance.iloc[1]["TARGET"]
count_0_perc = round(count_0/(count_0+count_1)*100,2)
count_1_perc = round(count_1/(count_0+count_1)*100,2)

print('Ratios of imbalance in percentage with respect to Repayer and Defaulter datas are: %.2f and %.2f'%(count_0_perc,count_1_perc))
print('Ratios of imbalance in relative with respect to Repayer and Defaulter datas is %.2f : 1 (approx)'%(count_0/count_1))

Ratios of imbalance in percentage with respect to Repayer and Defaulter datas are: 0.00 and 100.00
Ratios of imbalance in relative with respect to Repayer and Defaulter datas is 0.00 : 1 (approx)


# function for plotting repetitive countplots in univariate categorical analysis on applicationDF
# This function will create two subplots: 
# 1. Count plot of categorical column w.r.t TARGET; 
# 2. Percentage of defaulters within column

def univariate_categorical(feature,ylog=False,label_rotation=False,horizontal_layout=True):
    temp = applicationDF[feature].value_counts()
    df1 = pd.DataFrame({feature: temp.index,'Number of contracts': temp.values})

    # Calculate the percentage of target=1 per category value
    cat_perc = applicationDF[[feature, 'TARGET']].groupby([feature],as_index=False).mean()
    cat_perc["TARGET"] = cat_perc["TARGET"]*100
    cat_perc.sort_values(by='TARGET', ascending=False, inplace=True)
    
    if(horizontal_layout):
        fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12,6))
    else:
        fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(20,24))
        
    # 1. Subplot 1: Count plot of categorical column
    # sns.set_palette("Set2")
    s = sns.countplot(ax=ax1, 
                    x = feature, 
                    data=applicationDF,
                    hue ="TARGET",
                    order=cat_perc[feature],
                    palette=['g','r'])
    
    # Define common styling
    ax1.set_title(feature, fontdict={'fontsize' : 10, 'fontweight' : 3, 'color' : 'Blue'}) 
    ax1.legend(['Repayer','Defaulter'])
    
    # If the plot is not readable, use the log scale.
    if ylog:
        ax1.set_yscale('log')
        ax1.set_ylabel("Count (log)",fontdict={'fontsize' : 10, 'fontweight' : 3, 'color' : 'Blue'})   
    
    
    if(label_rotation):
        s.set_xticklabels(s.get_xticklabels(),rotation=90)
    
    # 2. Subplot 2: Percentage of defaulters within the categorical column
    s = sns.barplot(ax=ax2, 
                    x = feature, 
                    y='TARGET', 
                    order=cat_perc[feature], 
                    data=cat_perc,
                    palette='Set2')
    
    if(label_rotation):
        s.set_xticklabels(s.get_xticklabels(),rotation=90)
    plt.ylabel('Percent of Defaulters [%]', fontsize=10)
    plt.tick_params(axis='both', which='major', labelsize=10)
    ax2.set_title(feature + " Defaulter %", fontdict={'fontsize' : 15, 'fontweight' : 5, 'color' : 'Blue'}) 

    plt.show();


# function for plotting repetitive countplots in bivariate categorical analysis

def bivariate_bar(x,y,df,hue,figsize):
    
    plt.figure(figsize=figsize)
    sns.barplot(x=x,
                  y=y,
                  data=df, 
                  hue=hue, 
                  palette =['g','r'])     
        
    # Defining aesthetics of Labels and Title of the plot using style dictionaries
    plt.xlabel(x,fontdict={'fontsize' : 10, 'fontweight' : 3, 'color' : 'Blue'})    
    plt.ylabel(y,fontdict={'fontsize' : 10, 'fontweight' : 3, 'color' : 'Blue'})    
    plt.title(col, fontdict={'fontsize' : 15, 'fontweight' : 5, 'color' : 'Blue'}) 
    plt.xticks(rotation=90, ha='right')
    plt.legend(labels = ['Repayer','Defaulter'])
    plt.show()


# function for plotting repetitive rel plots in bivaritae numerical analysis on applicationDF

def bivariate_rel(x,y,data, hue, kind, palette, legend,figsize):
    
    plt.figure(figsize=figsize)
    sns.relplot(x=x, 
                y=y, 
                data=applicationDF, 
                hue="TARGET",
                kind=kind,
                palette = ['g','r'],
                legend = False)
    plt.legend(['Repayer','Defaulter'])
    plt.xticks(rotation=90, ha='right')
    plt.show()


#function for plotting repetitive countplots in univariate categorical analysis on the merged df

def univariate_merged(col,df,hue,palette,ylog,figsize):
    plt.figure(figsize=figsize)
    ax=sns.countplot(x=col, 
                  data=df,
                  hue= hue,
                  palette= palette,
                  order=df[col].value_counts().index)
    

    if ylog:
        plt.yscale('log')
        plt.ylabel("Count (log)",fontdict={'fontsize' : 10, 'fontweight' : 3, 'color' : 'Blue'})     
    else:
        plt.ylabel("Count",fontdict={'fontsize' : 10, 'fontweight' : 3, 'color' : 'Blue'})       

    plt.title(col , fontdict={'fontsize' : 15, 'fontweight' : 5, 'color' : 'Blue'}) 
    plt.legend(loc = "upper right")
    plt.xticks(rotation=90, ha='right')
    
    plt.show()


# Function to plot point plots on merged dataframe

def merged_pointplot(x,y):
    plt.figure(figsize=(8,4))
    sns.pointplot(x=x, 
                  y=y, 
                  hue="TARGET", 
                  data=loan_process_df,
                  palette =['g','r'])
   # plt.legend(['Repayer','Defaulter'])


# Checking the contract type based on loan repayment status
univariate_categorical('NAME_CONTRACT_TYPE',True)


# Checking the type of Gender on loan repayment status
univariate_categorical('CODE_GENDER')


# Checking if owning a car is related to loan repayment status
univariate_categorical('FLAG_OWN_CAR')


# Checking if owning a realty is related to loan repayment status
univariate_categorical('FLAG_OWN_REALTY')


# Analyzing Housing Type based on loan repayment status
univariate_categorical("NAME_HOUSING_TYPE",True,True,True)


# Analyzing Family status based on loan repayment status
univariate_categorical("NAME_FAMILY_STATUS",False,True,True)


# Analyzing Education Type based on loan repayment status
univariate_categorical("NAME_EDUCATION_TYPE",True,True,True)


# Analyzing Income Type based on loan repayment status
univariate_categorical("NAME_INCOME_TYPE",True,True,False)


# Analyzing Region rating where applicant lives based on loan repayment status
univariate_categorical("REGION_RATING_CLIENT",False,False,True)


# Analyzing Occupation Type where applicant lives based on loan repayment status
univariate_categorical("OCCUPATION_TYPE",False,True,False)


# Checking Loan repayment status based on Organization type
univariate_categorical("ORGANIZATION_TYPE",True,True,False)


# Analyzing Flag_Doc_3 submission status based on loan repayment status
univariate_categorical("FLAG_DOCUMENT_3",False,False,True)


# Analyzing Age Group based on loan repayment status
univariate_categorical("AGE_GROUP",False,False,True)


# Analyzing Employment_Year based on loan repayment status
univariate_categorical("EMPLOYMENT_YEAR",False,False,True)


# Analyzing Amount_Credit based on loan repayment status
univariate_categorical("AMT_CREDIT_RANGE",False,False,False)


# Analyzing Amount_Income Range based on loan repayment status
univariate_categorical("AMT_INCOME_RANGE",False,False,False)


# Analyzing Number of children based on loan repayment status
univariate_categorical("CNT_CHILDREN",True)


# Analyzing Number of family members based on loan repayment status
univariate_categorical("CNT_FAM_MEMBERS",True, False, False)


applicationDF.groupby('NAME_INCOME_TYPE')['AMT_INCOME_TOTAL'].describe()


# Income type vs Income Amount Range
bivariate_bar("NAME_INCOME_TYPE","AMT_INCOME_TOTAL",applicationDF,"TARGET",(18,10))


applicationDF.columns

Index(['SK_ID_CURR', 'TARGET', 'NAME_CONTRACT_TYPE', 'CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'CNT_CHILDREN', 'AMT_INCOME_TOTAL', 'AMT_CREDIT', 'AMT_ANNUITY', 'AMT_GOODS_PRICE', 'NAME_TYPE_SUITE', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'REGION_POPULATION_RELATIVE', 'DAYS_BIRTH', 'DAYS_EMPLOYED', 'DAYS_REGISTRATION', 'DAYS_ID_PUBLISH', 'OCCUPATION_TYPE', 'CNT_FAM_MEMBERS', 'REGION_RATING_CLIENT', 'REGION_RATING_CLIENT_W_CITY', 'WEEKDAY_APPR_PROCESS_START', 'HOUR_APPR_PROCESS_START', 'REG_REGION_NOT_LIVE_REGION', 'REG_REGION_NOT_WORK_REGION', 'LIVE_REGION_NOT_WORK_REGION', 'REG_CITY_NOT_LIVE_CITY', 'REG_CITY_NOT_WORK_CITY', 'LIVE_CITY_NOT_WORK_CITY', 'ORGANIZATION_TYPE', 'OBS_30_CNT_SOCIAL_CIRCLE', 'DEF_30_CNT_SOCIAL_CIRCLE', 'OBS_60_CNT_SOCIAL_CIRCLE', 'DEF_60_CNT_SOCIAL_CIRCLE', 'DAYS_LAST_PHONE_CHANGE', 'FLAG_DOCUMENT_3', 'AMT_REQ_CREDIT_BUREAU_HOUR', 'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK',
       'AMT_REQ_CREDIT_BUREAU_MON', 'AMT_REQ_CREDIT_BUREAU_QRT', 'AMT_REQ_CREDIT_BUREAU_YEAR', 'AMT_INCOME_RANGE', 'AMT_CREDIT_RANGE', 'AGE', 'AGE_GROUP', 'YEARS_EMPLOYED', 'EMPLOYMENT_YEAR'],
      dtype='object')


# Bifurcating the applicationDF dataframe based on Target value 0 and 1 for correlation and other analysis
cols_for_correlation = ['NAME_CONTRACT_TYPE', 'CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 
                        'CNT_CHILDREN', 'AMT_INCOME_TOTAL', 'AMT_CREDIT', 'AMT_ANNUITY', 'AMT_GOODS_PRICE', 
                        'NAME_TYPE_SUITE', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS',
                        'NAME_HOUSING_TYPE', 'REGION_POPULATION_RELATIVE', 'DAYS_BIRTH', 'DAYS_EMPLOYED', 
                        'DAYS_REGISTRATION', 'DAYS_ID_PUBLISH', 'OCCUPATION_TYPE', 'CNT_FAM_MEMBERS', 'REGION_RATING_CLIENT',
                        'REGION_RATING_CLIENT_W_CITY', 'WEEKDAY_APPR_PROCESS_START', 'HOUR_APPR_PROCESS_START',
                        'REG_REGION_NOT_LIVE_REGION', 'REG_REGION_NOT_WORK_REGION', 'LIVE_REGION_NOT_WORK_REGION', 
                        'REG_CITY_NOT_LIVE_CITY', 'REG_CITY_NOT_WORK_CITY', 'LIVE_CITY_NOT_WORK_CITY', 'ORGANIZATION_TYPE',
                        'OBS_60_CNT_SOCIAL_CIRCLE', 'DEF_60_CNT_SOCIAL_CIRCLE', 'DAYS_LAST_PHONE_CHANGE', 'FLAG_DOCUMENT_3', 
                        'AMT_REQ_CREDIT_BUREAU_HOUR', 'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK',
                        'AMT_REQ_CREDIT_BUREAU_MON', 'AMT_REQ_CREDIT_BUREAU_QRT', 'AMT_REQ_CREDIT_BUREAU_YEAR']


Repayer_df = applicationDF.loc[applicationDF['TARGET']==0, cols_for_correlation] # Repayers
Defaulter_df = applicationDF.loc[applicationDF['TARGET']==1, cols_for_correlation] # Defaulters


# Select only numeric columns
numeric_columns = Repayer_df.select_dtypes(include=np.number)

# Calculate correlations for numeric columns
corr_repayer = numeric_columns.corr()

# Remove duplicate correlations and keep only the upper triangular part
corr_repayer = corr_repayer.where(np.triu(np.ones(corr_repayer.shape), k=1).astype(bool))

# Reset index to obtain VAR1, VAR2, and Correlation in separate columns
corr_df_repayer = corr_repayer.unstack().reset_index()

# Rename columns
corr_df_repayer.columns = ['VAR1', 'VAR2', 'Correlation']

# Drop rows with NaN values in the 'Correlation' column
corr_df_repayer.dropna(subset=["Correlation"], inplace=True)

# Take absolute values of correlations
corr_df_repayer["Correlation"] = corr_df_repayer["Correlation"].abs()

# Sort by absolute correlation values in descending order
corr_df_repayer.sort_values(by='Correlation', ascending=False, inplace=True)

# Display the top 10 correlations
corr_df_repayer.head(10)


# Select only numeric columns
numeric_columns = Repayer_df.select_dtypes(include=np.number)

# Calculate correlation matrix for numeric columns
corr_matrix = numeric_columns.corr()

# Plot heatmap
fig = plt.figure(figsize=(12, 12))
ax = sns.heatmap(corr_matrix, cmap="RdYlGn", annot=False, linewidth=1)
plt.show()


# Select only numeric columns
numeric_columns = Defaulter_df.select_dtypes(include=np.number)

# Calculate correlation matrix for numeric columns
corr_matrix = numeric_columns.corr()

# Remove duplicate correlations and keep only the upper triangular part
corr_matrix = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))

# Reset index to obtain VAR1, VAR2, and Correlation in separate columns
corr_df_Defaulter = corr_matrix.unstack().reset_index()

# Rename columns
corr_df_Defaulter.columns = ['VAR1', 'VAR2', 'Correlation']

# Drop rows with NaN values in the 'Correlation' column
corr_df_Defaulter.dropna(subset=["Correlation"], inplace=True)

# Take absolute values of correlations
corr_df_Defaulter["Correlation"] = corr_df_Defaulter["Correlation"].abs()

# Sort by absolute correlation values in descending order
corr_df_Defaulter.sort_values(by='Correlation', ascending=False, inplace=True)

# Display the top 10 correlations
corr_df_Defaulter.head(10)


# Select only numeric columns
numeric_columns = Defaulter_df.select_dtypes(include=np.number)

# Calculate correlation matrix for numeric columns
corr_matrix = numeric_columns.corr()

# Plot heatmap
fig = plt.figure(figsize=(12, 12))
ax = sns.heatmap(corr_matrix, cmap="RdYlGn", annot=False, linewidths=1)
plt.show()


# Select only numeric columns
numeric_columns = Defaulter_df.select_dtypes(include=np.number)

# Calculate correlation matrix for numeric columns
corr_matrix = numeric_columns.corr()

# Remove duplicate correlations and keep only the upper triangular part
corr_matrix = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))

# Reset index to obtain VAR1, VAR2, and Correlation in separate columns
corr_df_Defaulter = corr_matrix.unstack().reset_index()

# Rename columns
corr_df_Defaulter.columns = ['VAR1', 'VAR2', 'Correlation']

# Drop rows with NaN values in the 'Correlation' column
corr_df_Defaulter.dropna(subset=["Correlation"], inplace=True)

# Take absolute values of correlations
corr_df_Defaulter["Correlation"] = corr_df_Defaulter["Correlation"].abs()

# Sort by absolute correlation values in descending order
corr_df_Defaulter.sort_values(by='Correlation', ascending=False, inplace=True)

# Display the top 10 correlations
corr_df_Defaulter.head(10)


# Select only numeric columns
numeric_columns = Defaulter_df.select_dtypes(include=np.number)

# Calculate correlation matrix for numeric columns
corr_matrix = numeric_columns.corr()

# Plot heatmap
fig = plt.figure(figsize=(12, 12))
ax = sns.heatmap(corr_matrix, cmap="RdYlGn", annot=False, linewidths=1)
plt.show()


# Plotting the numerical columns related to amount as distribution plot to see density
amount = applicationDF[[ 'AMT_INCOME_TOTAL','AMT_CREDIT','AMT_ANNUITY', 'AMT_GOODS_PRICE']]

fig = plt.figure(figsize=(16,12))

for i in enumerate(amount):
    plt.subplot(2,2,i[0]+1)
    sns.distplot(Defaulter_df[i[1]], hist=False, color='r',label ="Defaulter")
    sns.distplot(Repayer_df[i[1]], hist=False, color='g', label ="Repayer")
    plt.title(i[1], fontdict={'fontsize' : 15, 'fontweight' : 5, 'color' : 'Blue'}) 
    
plt.legend()

plt.show()


# Checking the relationship between Goods price and credit and comparing with loan repayment staus
bivariate_rel('AMT_GOODS_PRICE','AMT_CREDIT',applicationDF,"TARGET", "line", ['g','r'], False,(4,4))

<Figure size 400x400 with 0 Axes>


# Plotting pairplot between amount variable to draw reference against loan repayment status
amount = applicationDF[[ 'AMT_INCOME_TOTAL','AMT_CREDIT',
                         'AMT_ANNUITY', 'AMT_GOODS_PRICE','TARGET']]
amount = amount[(amount["AMT_GOODS_PRICE"].notnull()) & (amount["AMT_ANNUITY"].notnull())]
ax= sns.pairplot(amount,hue="TARGET",palette=["g","r"])
ax.fig.legend(labels=['Repayer','Defaulter'])
plt.show()


#merge both the dataframe on SK_ID_CURR with Inner Joins
loan_process_df = pd.merge(applicationDF, previousDF, how='inner', on='SK_ID_CURR')
loan_process_df.head()


#Checking the details of the merged dataframe
loan_process_df.shape

(1413701, 74)


# Checking the element count of the dataframe
loan_process_df.size

104613874


# checking the columns and column types of the dataframe
loan_process_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1413701 entries, 0 to 1413700
Data columns (total 74 columns):
 #   Column                       Non-Null Count    Dtype   
---  ------                       --------------    -----   
 0   SK_ID_CURR                   1413701 non-null  int64   
 1   TARGET                       1413701 non-null  int64   
 2   NAME_CONTRACT_TYPE_x         1413701 non-null  category
 3   CODE_GENDER                  1413701 non-null  category
 4   FLAG_OWN_CAR                 1413701 non-null  category
 5   FLAG_OWN_REALTY              1413701 non-null  category
 6   CNT_CHILDREN                 1413701 non-null  int64   
 7   AMT_INCOME_TOTAL             1413701 non-null  float64 
 8   AMT_CREDIT_x                 1413701 non-null  float64 
 9   AMT_ANNUITY_x                1413608 non-null  float64 
 10  AMT_GOODS_PRICE_x            1412493 non-null  float64 
 11  NAME_TYPE_SUITE              1413701 non-null  category
 12  NAME_INCOME_TYPE             1413701 non-null  category
 13  NAME_EDUCATION_TYPE          1413701 non-null  category
 14  NAME_FAMILY_STATUS           1413701 non-null  category
 15  NAME_HOUSING_TYPE            1413701 non-null  category
 16  REGION_POPULATION_RELATIVE   1413701 non-null  float64 
 17  DAYS_BIRTH                   1413701 non-null  int64   
 18  DAYS_EMPLOYED                1413701 non-null  int64   
 19  DAYS_REGISTRATION            1413701 non-null  float64 
 20  DAYS_ID_PUBLISH              1413701 non-null  int64   
 21  OCCUPATION_TYPE              1413701 non-null  category
 22  CNT_FAM_MEMBERS              1413701 non-null  float64 
 23  REGION_RATING_CLIENT         1413701 non-null  category
 24  REGION_RATING_CLIENT_W_CITY  1413701 non-null  category
 25  WEEKDAY_APPR_PROCESS_START   1413701 non-null  category
 26  HOUR_APPR_PROCESS_START      1413701 non-null  int64   
 27  REG_REGION_NOT_LIVE_REGION   1413701 non-null  int64   
 28  REG_REGION_NOT_WORK_REGION   1413701 non-null  category
 29  LIVE_REGION_NOT_WORK_REGION  1413701 non-null  category
 30  REG_CITY_NOT_LIVE_CITY       1413701 non-null  category
 31  REG_CITY_NOT_WORK_CITY       1413701 non-null  category
 32  LIVE_CITY_NOT_WORK_CITY      1413701 non-null  category
 33  ORGANIZATION_TYPE            1413701 non-null  category
 34  OBS_30_CNT_SOCIAL_CIRCLE     1410555 non-null  float64 
 35  DEF_30_CNT_SOCIAL_CIRCLE     1410555 non-null  float64 
 36  OBS_60_CNT_SOCIAL_CIRCLE     1410555 non-null  float64 
 37  DEF_60_CNT_SOCIAL_CIRCLE     1410555 non-null  float64 
 38  DAYS_LAST_PHONE_CHANGE       1413701 non-null  float64 
 39  FLAG_DOCUMENT_3              1413701 non-null  int64   
 40  AMT_REQ_CREDIT_BUREAU_HOUR   1413701 non-null  float64 
 41  AMT_REQ_CREDIT_BUREAU_DAY    1413701 non-null  float64 
 42  AMT_REQ_CREDIT_BUREAU_WEEK   1413701 non-null  float64 
 43  AMT_REQ_CREDIT_BUREAU_MON    1413701 non-null  float64 
 44  AMT_REQ_CREDIT_BUREAU_QRT    1413701 non-null  float64 
 45  AMT_REQ_CREDIT_BUREAU_YEAR   1413701 non-null  float64 
 46  AMT_INCOME_RANGE             1413024 non-null  category
 47  AMT_CREDIT_RANGE             1413701 non-null  category
 48  AGE                          1413701 non-null  int64   
 49  AGE_GROUP                    1413701 non-null  category
 50  YEARS_EMPLOYED               1413701 non-null  int64   
 51  EMPLOYMENT_YEAR              1032756 non-null  category
 52  SK_ID_PREV                   1413701 non-null  int64   
 53  NAME_CONTRACT_TYPE_y         1413701 non-null  category
 54  AMT_ANNUITY_y                1413701 non-null  float64 
 55  AMT_APPLICATION              1413701 non-null  float64 
 56  AMT_CREDIT_y                 1413700 non-null  float64 
 57  AMT_GOODS_PRICE_y            1413701 non-null  float64 
 58  NAME_CASH_LOAN_PURPOSE       1413701 non-null  category
 59  NAME_CONTRACT_STATUS         1413701 non-null  category
 60  DAYS_DECISION                1413701 non-null  int64   
 61  NAME_PAYMENT_TYPE            1413701 non-null  category
 62  CODE_REJECT_REASON           1413701 non-null  category
 63  NAME_CLIENT_TYPE             1413701 non-null  category
 64  NAME_GOODS_CATEGORY          1413701 non-null  category
 65  NAME_PORTFOLIO               1413701 non-null  category
 66  NAME_PRODUCT_TYPE            1413701 non-null  category
 67  CHANNEL_TYPE                 1413701 non-null  category
 68  SELLERPLACE_AREA             1413701 non-null  int64   
 69  NAME_SELLER_INDUSTRY         1413701 non-null  category
 70  CNT_PAYMENT                  1106488 non-null  float64 
 71  NAME_YIELD_GROUP             1413701 non-null  category
 72  PRODUCT_COMBINATION          1413388 non-null  category
 73  DAYS_DECISION_GROUP          1413701 non-null  category
dtypes: category(37), float64(23), int64(14)
memory usage: 449.0 MB


# Checking merged dataframe numerical columns statistics
loan_process_df.describe()


# Bifurcating the applicationDF dataframe based on Target value 0 and 1 for correlation and other analysis

L0 = loan_process_df[loan_process_df['TARGET']==0] # Repayers
L1 = loan_process_df[loan_process_df['TARGET']==1] # Defaulters


univariate_merged("NAME_CASH_LOAN_PURPOSE",L0,"NAME_CONTRACT_STATUS",["#548235","#FF0000","#0070C0","#FFFF00"],True,(18,7))

univariate_merged("NAME_CASH_LOAN_PURPOSE",L1,"NAME_CONTRACT_STATUS",["#548235","#FF0000","#0070C0","#FFFF00"],True,(18,7))


# Checking the Contract Status based on loan repayment status and whether there is any business loss or financial loss
univariate_merged("NAME_CONTRACT_STATUS",loan_process_df,"TARGET",['g','r'],False,(12,8))
g = loan_process_df.groupby("NAME_CONTRACT_STATUS")["TARGET"]
df1 = pd.concat([g.value_counts(),round(g.value_counts(normalize=True).mul(100),2)],axis=1, keys=('Counts','Percentage'))
df1['Percentage'] = df1['Percentage'].astype(str) +"%" # adding percentage symbol in the results for understanding
print (df1)

                             Counts Percentage
NAME_CONTRACT_STATUS TARGET                   
Approved             0       818856     92.41%
                     1        67243      7.59%
Canceled             0       235641     90.83%
                     1        23800      9.17%
Refused              0       215952      88.0%
                     1        29438      12.0%
Unused offer         0        20892     91.75%
                     1         1879      8.25%


# plotting the relationship between income total and contact status
merged_pointplot("NAME_CONTRACT_STATUS",'AMT_INCOME_TOTAL')


# plotting the relationship between people who defaulted in last 60 days being in client's social circle and contact status
merged_pointplot("NAME_CONTRACT_STATUS",'DEF_60_CNT_SOCIAL_CIRCLE')

	SK_ID_PREV	SK_ID_CURR	NAME_CONTRACT_TYPE	AMT_ANNUITY	AMT_APPLICATION	AMT_CREDIT	AMT_DOWN_PAYMENT	AMT_GOODS_PRICE	WEEKDAY_APPR_PROCESS_START	HOUR_APPR_PROCESS_START	FLAG_LAST_APPL_PER_CONTRACT	NFLAG_LAST_APPL_IN_DAY	RATE_DOWN_PAYMENT	RATE_INTEREST_PRIMARY	RATE_INTEREST_PRIVILEGED	NAME_CASH_LOAN_PURPOSE	NAME_CONTRACT_STATUS	DAYS_DECISION	NAME_PAYMENT_TYPE	CODE_REJECT_REASON	NAME_TYPE_SUITE	NAME_CLIENT_TYPE	NAME_GOODS_CATEGORY	NAME_PORTFOLIO	NAME_PRODUCT_TYPE	CHANNEL_TYPE	SELLERPLACE_AREA	NAME_SELLER_INDUSTRY	CNT_PAYMENT	NAME_YIELD_GROUP	PRODUCT_COMBINATION	DAYS_FIRST_DRAWING	DAYS_FIRST_DUE	DAYS_LAST_DUE_1ST_VERSION	DAYS_LAST_DUE	DAYS_TERMINATION	NFLAG_INSURED_ON_APPROVAL
0	2030495	271877	Consumer loans	1730.430	17145.0	17145.0	0.0	17145.0	SATURDAY	15	Y	1	0.0	0.182832	0.867336	XAP	Approved	-73	Cash through the bank	XAP	NaN	Repeater	Mobile	POS	XNA	Country-wide	35	Connectivity	12.0	middle	POS mobile with interest	365243.0	-42.0	300.0	-42.0	-37.0	0.0
1	2802425	108129	Cash loans	25188.615	607500.0	679671.0	NaN	607500.0	THURSDAY	11	Y	1	NaN	NaN	NaN	XNA	Approved	-164	XNA	XAP	Unaccompanied	Repeater	XNA	Cash	x-sell	Contact center	-1	XNA	36.0	low_action	Cash X-Sell: low	365243.0	-134.0	916.0	365243.0	365243.0	1.0
2	2523466	122040	Cash loans	15060.735	112500.0	136444.5	NaN	112500.0	TUESDAY	11	Y	1	NaN	NaN	NaN	XNA	Approved	-301	Cash through the bank	XAP	Spouse, partner	Repeater	XNA	Cash	x-sell	Credit and cash offices	-1	XNA	12.0	high	Cash X-Sell: high	365243.0	-271.0	59.0	365243.0	365243.0	1.0
3	2819243	176158	Cash loans	47041.335	450000.0	470790.0	NaN	450000.0	MONDAY	7	Y	1	NaN	NaN	NaN	XNA	Approved	-512	Cash through the bank	XAP	NaN	Repeater	XNA	Cash	x-sell	Credit and cash offices	-1	XNA	12.0	middle	Cash X-Sell: middle	365243.0	-482.0	-152.0	-182.0	-177.0	1.0
4	1784265	202054	Cash loans	31924.395	337500.0	404055.0	NaN	337500.0	THURSDAY	9	Y	1	NaN	NaN	NaN	Repairs	Refused	-781	Cash through the bank	HC	NaN	Repeater	XNA	Cash	walk-in	Credit and cash offices	-1	XNA	24.0	high	Cash Street: high	NaN	NaN	NaN	NaN	NaN	NaN

	SK_ID_CURR	TARGET	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	DAYS_ID_PUBLISH	OWN_CAR_AGE	FLAG_MOBIL	FLAG_EMP_PHONE	FLAG_WORK_PHONE	FLAG_CONT_MOBILE	FLAG_PHONE	FLAG_EMAIL	CNT_FAM_MEMBERS	REGION_RATING_CLIENT	REGION_RATING_CLIENT_W_CITY	HOUR_APPR_PROCESS_START	REG_REGION_NOT_LIVE_REGION	REG_REGION_NOT_WORK_REGION	LIVE_REGION_NOT_WORK_REGION	REG_CITY_NOT_LIVE_CITY	REG_CITY_NOT_WORK_CITY	LIVE_CITY_NOT_WORK_CITY	EXT_SOURCE_1	EXT_SOURCE_2	EXT_SOURCE_3	APARTMENTS_AVG	BASEMENTAREA_AVG	YEARS_BEGINEXPLUATATION_AVG	YEARS_BUILD_AVG	COMMONAREA_AVG	ELEVATORS_AVG	ENTRANCES_AVG	FLOORSMAX_AVG	FLOORSMIN_AVG	LANDAREA_AVG	LIVINGAPARTMENTS_AVG	LIVINGAREA_AVG	NONLIVINGAPARTMENTS_AVG	NONLIVINGAREA_AVG	APARTMENTS_MODE	BASEMENTAREA_MODE	YEARS_BEGINEXPLUATATION_MODE	YEARS_BUILD_MODE	COMMONAREA_MODE	ELEVATORS_MODE	ENTRANCES_MODE	FLOORSMAX_MODE	FLOORSMIN_MODE	LANDAREA_MODE	LIVINGAPARTMENTS_MODE	LIVINGAREA_MODE	NONLIVINGAPARTMENTS_MODE	NONLIVINGAREA_MODE	APARTMENTS_MEDI	BASEMENTAREA_MEDI	YEARS_BEGINEXPLUATATION_MEDI	YEARS_BUILD_MEDI	COMMONAREA_MEDI	ELEVATORS_MEDI	ENTRANCES_MEDI	FLOORSMAX_MEDI	FLOORSMIN_MEDI	LANDAREA_MEDI	LIVINGAPARTMENTS_MEDI	LIVINGAREA_MEDI	NONLIVINGAPARTMENTS_MEDI	NONLIVINGAREA_MEDI	TOTALAREA_MODE	OBS_30_CNT_SOCIAL_CIRCLE	DEF_30_CNT_SOCIAL_CIRCLE	OBS_60_CNT_SOCIAL_CIRCLE	DEF_60_CNT_SOCIAL_CIRCLE	DAYS_LAST_PHONE_CHANGE	FLAG_DOCUMENT_2	FLAG_DOCUMENT_3	FLAG_DOCUMENT_4	FLAG_DOCUMENT_5	FLAG_DOCUMENT_6	FLAG_DOCUMENT_7	FLAG_DOCUMENT_8	FLAG_DOCUMENT_9	FLAG_DOCUMENT_10	FLAG_DOCUMENT_11	FLAG_DOCUMENT_12	FLAG_DOCUMENT_13	FLAG_DOCUMENT_14	FLAG_DOCUMENT_15	FLAG_DOCUMENT_16	FLAG_DOCUMENT_17	FLAG_DOCUMENT_18	FLAG_DOCUMENT_19	FLAG_DOCUMENT_20	FLAG_DOCUMENT_21	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
count	307511.000000	307511.000000	307511.000000	3.075110e+05	3.075110e+05	307499.000000	3.072330e+05	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	104582.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307509.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	134133.000000	3.068510e+05	246546.000000	151450.00000	127568.000000	157504.000000	103023.000000	92646.000000	143620.000000	152683.000000	154491.000000	98869.000000	124921.000000	97312.000000	153161.000000	93997.000000	137829.000000	151450.000000	127568.000000	157504.000000	103023.000000	92646.000000	143620.000000	152683.000000	154491.000000	98869.000000	124921.000000	97312.000000	153161.000000	93997.000000	137829.000000	151450.000000	127568.000000	157504.000000	103023.000000	92646.000000	143620.000000	152683.000000	154491.000000	98869.000000	124921.000000	97312.000000	153161.000000	93997.000000	137829.000000	159080.000000	306490.000000	306490.000000	306490.000000	306490.000000	307510.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.00000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	265992.000000	265992.000000	265992.000000	265992.000000	265992.000000	265992.000000
mean	278180.518577	0.080729	0.417052	1.687979e+05	5.990260e+05	27108.573909	5.383962e+05	0.020868	-16036.995067	63815.045904	-4986.120328	-2994.202373	12.061091	0.999997	0.819889	0.199368	0.998133	0.281066	0.056720	2.152665	2.052463	2.031521	12.063419	0.015144	0.050769	0.040659	0.078173	0.230454	0.179555	0.502130	5.143927e-01	0.510853	0.11744	0.088442	0.977735	0.752471	0.044621	0.078942	0.149725	0.226282	0.231894	0.066333	0.100775	0.107399	0.008809	0.028358	0.114231	0.087543	0.977065	0.759637	0.042553	0.074490	0.145193	0.222315	0.228058	0.064958	0.105645	0.105975	0.008076	0.027022	0.117850	0.087955	0.977752	0.755746	0.044595	0.078078	0.149213	0.225897	0.231625	0.067169	0.101954	0.108607	0.008651	0.028236	0.102547	1.422245	0.143421	1.405292	0.100049	-962.858788	0.000042	0.710023	0.000081	0.015115	0.088055	0.000192	0.081376	0.003896	0.000023	0.003912	0.000007	0.003525	0.002936	0.00121	0.009928	0.000267	0.008130	0.000595	0.000507	0.000335	0.006402	0.007000	0.034362	0.267395	0.265474	1.899974
std	102790.175348	0.272419	0.722121	2.371231e+05	4.024908e+05	14493.737315	3.694465e+05	0.013831	4363.988632	141275.766519	3522.886321	1509.450419	11.944812	0.001803	0.384280	0.399526	0.043164	0.449521	0.231307	0.910682	0.509034	0.502737	3.265832	0.122126	0.219526	0.197499	0.268444	0.421124	0.383817	0.211062	1.910602e-01	0.194844	0.10824	0.082438	0.059223	0.113280	0.076036	0.134576	0.100049	0.144641	0.161380	0.081184	0.092576	0.110565	0.047732	0.069523	0.107936	0.084307	0.064575	0.110111	0.074445	0.132256	0.100977	0.143709	0.161160	0.081750	0.097880	0.111845	0.046276	0.070254	0.109076	0.082179	0.059897	0.112066	0.076144	0.134467	0.100368	0.145067	0.161934	0.082167	0.093642	0.112260	0.047415	0.070166	0.107462	2.400989	0.446698	2.379803	0.362291	826.808487	0.006502	0.453752	0.009016	0.122010	0.283376	0.013850	0.273412	0.062295	0.004771	0.062424	0.002550	0.059268	0.054110	0.03476	0.099144	0.016327	0.089798	0.024387	0.022518	0.018299	0.083849	0.110757	0.204685	0.916002	0.794056	1.869295
min	100002.000000	0.000000	0.000000	2.565000e+04	4.500000e+04	1615.500000	4.050000e+04	0.000290	-25229.000000	-17912.000000	-24672.000000	-7197.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.014568	8.173617e-08	0.000527	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	-4292.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	189145.500000	0.000000	0.000000	1.125000e+05	2.700000e+05	16524.000000	2.385000e+05	0.010006	-19682.000000	-2760.000000	-7479.500000	-4299.000000	5.000000	1.000000	1.000000	0.000000	1.000000	0.000000	0.000000	2.000000	2.000000	2.000000	10.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.334007	3.924574e-01	0.370650	0.05770	0.044200	0.976700	0.687200	0.007800	0.000000	0.069000	0.166700	0.083300	0.018700	0.050400	0.045300	0.000000	0.000000	0.052500	0.040700	0.976700	0.699400	0.007200	0.000000	0.069000	0.166700	0.083300	0.016600	0.054200	0.042700	0.000000	0.000000	0.058300	0.043700	0.976700	0.691400	0.007900	0.000000	0.069000	0.166700	0.083300	0.018700	0.051300	0.045700	0.000000	0.000000	0.041200	0.000000	0.000000	0.000000	0.000000	-1570.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	278202.000000	0.000000	0.000000	1.471500e+05	5.135310e+05	24903.000000	4.500000e+05	0.018850	-15750.000000	-1213.000000	-4504.000000	-3254.000000	9.000000	1.000000	1.000000	0.000000	1.000000	0.000000	0.000000	2.000000	2.000000	2.000000	12.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.505998	5.659614e-01	0.535276	0.08760	0.076300	0.981600	0.755200	0.021100	0.000000	0.137900	0.166700	0.208300	0.048100	0.075600	0.074500	0.000000	0.003600	0.084000	0.074600	0.981600	0.764800	0.019000	0.000000	0.137900	0.166700	0.208300	0.045800	0.077100	0.073100	0.000000	0.001100	0.086400	0.075800	0.981600	0.758500	0.020800	0.000000	0.137900	0.166700	0.208300	0.048700	0.076100	0.074900	0.000000	0.003100	0.068800	0.000000	0.000000	0.000000	0.000000	-757.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000
75%	367142.500000	0.000000	1.000000	2.025000e+05	8.086500e+05	34596.000000	6.795000e+05	0.028663	-12413.000000	-289.000000	-2010.000000	-1720.000000	15.000000	1.000000	1.000000	0.000000	1.000000	1.000000	0.000000	3.000000	2.000000	2.000000	14.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.675053	6.636171e-01	0.669057	0.14850	0.112200	0.986600	0.823200	0.051500	0.120000	0.206900	0.333300	0.375000	0.085600	0.121000	0.129900	0.003900	0.027700	0.143900	0.112400	0.986600	0.823600	0.049000	0.120800	0.206900	0.333300	0.375000	0.084100	0.131300	0.125200	0.003900	0.023100	0.148900	0.111600	0.986600	0.825600	0.051300	0.120000	0.206900	0.333300	0.375000	0.086800	0.123100	0.130300	0.003900	0.026600	0.127600	2.000000	0.000000	2.000000	0.000000	-274.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.000000
max	456255.000000	1.000000	19.000000	1.170000e+08	4.050000e+06	258025.500000	4.050000e+06	0.072508	-7489.000000	365243.000000	0.000000	0.000000	91.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	20.000000	3.000000	3.000000	23.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.962693	8.549997e-01	0.896010	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	348.000000	34.000000	344.000000	24.000000	0.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	4.000000	9.000000	8.000000	27.000000	261.000000	25.000000

	SK_ID_PREV	SK_ID_CURR	AMT_ANNUITY	AMT_APPLICATION	AMT_CREDIT	AMT_DOWN_PAYMENT	AMT_GOODS_PRICE	HOUR_APPR_PROCESS_START	NFLAG_LAST_APPL_IN_DAY	RATE_DOWN_PAYMENT	RATE_INTEREST_PRIMARY	RATE_INTEREST_PRIVILEGED	DAYS_DECISION	SELLERPLACE_AREA	CNT_PAYMENT	DAYS_FIRST_DRAWING	DAYS_FIRST_DUE	DAYS_LAST_DUE_1ST_VERSION	DAYS_LAST_DUE	DAYS_TERMINATION	NFLAG_INSURED_ON_APPROVAL
count	1.670214e+06	1.670214e+06	1.297979e+06	1.670214e+06	1.670213e+06	7.743700e+05	1.284699e+06	1.670214e+06	1.670214e+06	774370.000000	5951.000000	5951.000000	1.670214e+06	1.670214e+06	1.297984e+06	997149.000000	997149.000000	997149.000000	997149.000000	997149.000000	997149.000000
mean	1.923089e+06	2.783572e+05	1.595512e+04	1.752339e+05	1.961140e+05	6.697402e+03	2.278473e+05	1.248418e+01	9.964675e-01	0.079637	0.188357	0.773503	-8.806797e+02	3.139511e+02	1.605408e+01	342209.855039	13826.269337	33767.774054	76582.403064	81992.343838	0.332570
std	5.325980e+05	1.028148e+05	1.478214e+04	2.927798e+05	3.185746e+05	2.092150e+04	3.153966e+05	3.334028e+00	5.932963e-02	0.107823	0.087671	0.100879	7.790997e+02	7.127443e+03	1.456729e+01	88916.115834	72444.869708	106857.034789	149647.415123	153303.516729	0.471134
min	1.000001e+06	1.000010e+05	0.000000e+00	0.000000e+00	0.000000e+00	-9.000000e-01	0.000000e+00	0.000000e+00	0.000000e+00	-0.000015	0.034781	0.373150	-2.922000e+03	-1.000000e+00	0.000000e+00	-2922.000000	-2892.000000	-2801.000000	-2889.000000	-2874.000000	0.000000
25%	1.461857e+06	1.893290e+05	6.321780e+03	1.872000e+04	2.416050e+04	0.000000e+00	5.084100e+04	1.000000e+01	1.000000e+00	0.000000	0.160716	0.715645	-1.300000e+03	-1.000000e+00	6.000000e+00	365243.000000	-1628.000000	-1242.000000	-1314.000000	-1270.000000	0.000000
50%	1.923110e+06	2.787145e+05	1.125000e+04	7.104600e+04	8.054100e+04	1.638000e+03	1.123200e+05	1.200000e+01	1.000000e+00	0.051605	0.189122	0.835095	-5.810000e+02	3.000000e+00	1.200000e+01	365243.000000	-831.000000	-361.000000	-537.000000	-499.000000	0.000000
75%	2.384280e+06	3.675140e+05	2.065842e+04	1.803600e+05	2.164185e+05	7.740000e+03	2.340000e+05	1.500000e+01	1.000000e+00	0.108909	0.193330	0.852537	-2.800000e+02	8.200000e+01	2.400000e+01	365243.000000	-411.000000	129.000000	-74.000000	-44.000000	1.000000
max	2.845382e+06	4.562550e+05	4.180581e+05	6.905160e+06	6.905160e+06	3.060045e+06	6.905160e+06	2.300000e+01	1.000000e+00	1.000000	1.000000	1.000000	-1.000000e+00	4.000000e+06	8.400000e+01	365243.000000	365243.000000	365243.000000	365243.000000	365243.000000	1.000000

	AMT_ANNUITY	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_GOODS_PRICE	DAYS_BIRTH	CNT_CHILDREN	DAYS_EMPLOYED
count	307499.000000	307511.000000	307511.000000	3.072330e+05	307511.000000	307511.000000	307511.000000
mean	27108.573909	1.687979	5.990260	5.383962e+05	16036.995067	0.417052	67724.742149
std	14493.737315	2.371231	4.024908	3.694465e+05	4363.988632	0.722121	139443.751806
min	1615.500000	0.256500	0.450000	4.050000e+04	7489.000000	0.000000	0.000000
25%	16524.000000	1.125000	2.700000	2.385000e+05	12413.000000	0.000000	933.000000
50%	24903.000000	1.471500	5.135310	4.500000e+05	15750.000000	0.000000	2219.000000
75%	34596.000000	2.025000	8.086500	6.795000e+05	19682.000000	1.000000	5707.000000
max	258025.500000	1170.000000	40.500000	4.050000e+06	25229.000000	19.000000	365243.000000

	AMT_ANNUITY	AMT_APPLICATION	AMT_CREDIT	AMT_GOODS_PRICE	SELLERPLACE_AREA	CNT_PAYMENT	DAYS_DECISION
count	1.670214e+06	1.670214e+06	1.670213e+06	1.670214e+06	1.670214e+06	1.297984e+06	1.670214e+06
mean	1.490651e+04	1.752339e+05	1.961140e+05	1.856429e+05	3.139511e+02	1.605408e+01	8.806797e+02
std	1.317751e+04	2.927798e+05	3.185746e+05	2.871413e+05	7.127443e+03	1.456729e+01	7.790997e+02
min	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	-1.000000e+00	0.000000e+00	1.000000e+00
25%	7.547096e+03	1.872000e+04	2.416050e+04	4.500000e+04	-1.000000e+00	6.000000e+00	2.800000e+02
50%	1.125000e+04	7.104600e+04	8.054100e+04	7.105050e+04	3.000000e+00	1.200000e+01	5.810000e+02
75%	1.682403e+04	1.803600e+05	2.164185e+05	1.804050e+05	8.200000e+01	2.400000e+01	1.300000e+03
max	4.180581e+05	6.905160e+06	6.905160e+06	6.905160e+06	4.000000e+06	8.400000e+01	2.922000e+03

	SK_ID_CURR	TARGET	NAME_CONTRACT_TYPE	CODE_GENDER	FLAG_OWN_CAR	FLAG_OWN_REALTY	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	NAME_TYPE_SUITE	NAME_INCOME_TYPE	NAME_EDUCATION_TYPE	NAME_FAMILY_STATUS	NAME_HOUSING_TYPE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	DAYS_ID_PUBLISH	OWN_CAR_AGE	FLAG_MOBIL	FLAG_EMP_PHONE	FLAG_WORK_PHONE	FLAG_CONT_MOBILE	FLAG_PHONE	OCCUPATION_TYPE	CNT_FAM_MEMBERS	REGION_RATING_CLIENT	REGION_RATING_CLIENT_W_CITY	WEEKDAY_APPR_PROCESS_START	HOUR_APPR_PROCESS_START	REG_CITY_NOT_WORK_CITY	LIVE_CITY_NOT_WORK_CITY	ORGANIZATION_TYPE	EXT_SOURCE_1	EXT_SOURCE_2	EXT_SOURCE_3	APARTMENTS_AVG	BASEMENTAREA_AVG	YEARS_BEGINEXPLUATATION_AVG	YEARS_BUILD_AVG	COMMONAREA_AVG	ELEVATORS_AVG	ENTRANCES_AVG	FLOORSMAX_AVG	FLOORSMIN_AVG	LANDAREA_AVG	LIVINGAPARTMENTS_AVG	LIVINGAREA_AVG	NONLIVINGAPARTMENTS_AVG	NONLIVINGAREA_AVG	APARTMENTS_MODE	BASEMENTAREA_MODE	YEARS_BEGINEXPLUATATION_MODE	YEARS_BUILD_MODE	COMMONAREA_MODE	ELEVATORS_MODE	ENTRANCES_MODE	FLOORSMAX_MODE	FLOORSMIN_MODE	LANDAREA_MODE	LIVINGAPARTMENTS_MODE	LIVINGAREA_MODE	NONLIVINGAPARTMENTS_MODE	NONLIVINGAREA_MODE	APARTMENTS_MEDI	BASEMENTAREA_MEDI	YEARS_BEGINEXPLUATATION_MEDI	YEARS_BUILD_MEDI	COMMONAREA_MEDI	ELEVATORS_MEDI	ENTRANCES_MEDI	FLOORSMAX_MEDI	FLOORSMIN_MEDI	LANDAREA_MEDI	LIVINGAPARTMENTS_MEDI	LIVINGAREA_MEDI	NONLIVINGAPARTMENTS_MEDI	NONLIVINGAREA_MEDI	FONDKAPREMONT_MODE	HOUSETYPE_MODE	TOTALAREA_MODE	WALLSMATERIAL_MODE	EMERGENCYSTATE_MODE	OBS_30_CNT_SOCIAL_CIRCLE	DEF_30_CNT_SOCIAL_CIRCLE	OBS_60_CNT_SOCIAL_CIRCLE	DEF_60_CNT_SOCIAL_CIRCLE	DAYS_LAST_PHONE_CHANGE	FLAG_DOCUMENT_3	FLAG_DOCUMENT_8	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
0	100002	1	Cash loans	M	N	Y	202500.0	406597.5	24700.5	351000.0	Unaccompanied	Working	Secondary / secondary special	Single / not married	House / apartment	0.018801	-9461	-637	-3648.0	-2120	NaN	1	1	0	1	1	Laborers	1.0	2	2	WEDNESDAY	10	0	0	Business Entity Type 3	0.083037	0.262949	0.139376	0.0247	0.0369	0.9722	0.6192	0.0143	0.00	0.0690	0.0833	0.1250	0.0369	0.0202	0.0190	0.0000	0.0000	0.0252	0.0383	0.9722	0.6341	0.0144	0.0000	0.0690	0.0833	0.1250	0.0377	0.022	0.0198	0.0	0.0	0.0250	0.0369	0.9722	0.6243	0.0144	0.00	0.0690	0.0833	0.1250	0.0375	0.0205	0.0193	0.0000	0.00	reg oper account	block of flats	0.0149	Stone, brick	No	2.0	2.0	2.0	2.0	-1134.0	1	0	0.0	0.0	0.0	0.0	0.0	1.0
1	100003	0	Cash loans	F	N	N	270000.0	1293502.5	35698.5	1129500.0	Family	State servant	Higher education	Married	House / apartment	0.003541	-16765	-1188	-1186.0	-291	NaN	1	1	0	1	1	Core staff	2.0	1	1	MONDAY	11	0	0	School	0.311267	0.622246	NaN	0.0959	0.0529	0.9851	0.7960	0.0605	0.08	0.0345	0.2917	0.3333	0.0130	0.0773	0.0549	0.0039	0.0098	0.0924	0.0538	0.9851	0.8040	0.0497	0.0806	0.0345	0.2917	0.3333	0.0128	0.079	0.0554	0.0	0.0	0.0968	0.0529	0.9851	0.7987	0.0608	0.08	0.0345	0.2917	0.3333	0.0132	0.0787	0.0558	0.0039	0.01	reg oper account	block of flats	0.0714	Block	No	1.0	0.0	1.0	0.0	-828.0	1	0	0.0	0.0	0.0	0.0	0.0	0.0
2	100004	0	Revolving loans	M	Y	Y	67500.0	135000.0	6750.0	135000.0	Unaccompanied	Working	Secondary / secondary special	Single / not married	House / apartment	0.010032	-19046	-225	-4260.0	-2531	26.0	1	1	1	1	1	Laborers	1.0	2	2	MONDAY	9	0	0	Government	NaN	0.555912	0.729567	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	0.0	0.0	0.0	-815.0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3	100006	0	Cash loans	F	N	Y	135000.0	312682.5	29686.5	297000.0	Unaccompanied	Working	Secondary / secondary special	Civil marriage	House / apartment	0.008019	-19005	-3039	-9833.0	-2437	NaN	1	1	0	1	0	Laborers	2.0	2	2	WEDNESDAY	17	0	0	Business Entity Type 3	NaN	0.650442	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.0	0.0	2.0	0.0	-617.0	1	0	NaN	NaN	NaN	NaN	NaN	NaN
4	100007	0	Cash loans	M	N	Y	121500.0	513000.0	21865.5	513000.0	Unaccompanied	Working	Secondary / secondary special	Single / not married	House / apartment	0.028663	-19932	-3038	-4311.0	-3458	NaN	1	1	0	1	0	Core staff	1.0	2	2	THURSDAY	11	1	1	Religion	NaN	0.322738	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	0.0	0.0	0.0	-1106.0	0	1	0.0	0.0	0.0	0.0	0.0	0.0

	Column Name	Null Values Percentage
21	OWN_CAR_AGE	65.990810
41	EXT_SOURCE_1	56.381073
44	APARTMENTS_AVG	50.749729
45	BASEMENTAREA_AVG	58.515956
46	YEARS_BEGINEXPLUATATION_AVG	48.781019
47	YEARS_BUILD_AVG	66.497784
48	COMMONAREA_AVG	69.872297
49	ELEVATORS_AVG	53.295980
50	ENTRANCES_AVG	50.348768
51	FLOORSMAX_AVG	49.760822
52	FLOORSMIN_AVG	67.848630
53	LANDAREA_AVG	59.376738
54	LIVINGAPARTMENTS_AVG	68.354953
55	LIVINGAREA_AVG	50.193326
56	NONLIVINGAPARTMENTS_AVG	69.432963
57	NONLIVINGAREA_AVG	55.179164
58	APARTMENTS_MODE	50.749729
59	BASEMENTAREA_MODE	58.515956
60	YEARS_BEGINEXPLUATATION_MODE	48.781019
61	YEARS_BUILD_MODE	66.497784
62	COMMONAREA_MODE	69.872297
63	ELEVATORS_MODE	53.295980
64	ENTRANCES_MODE	50.348768
65	FLOORSMAX_MODE	49.760822
66	FLOORSMIN_MODE	67.848630
67	LANDAREA_MODE	59.376738
68	LIVINGAPARTMENTS_MODE	68.354953
69	LIVINGAREA_MODE	50.193326
70	NONLIVINGAPARTMENTS_MODE	69.432963
71	NONLIVINGAREA_MODE	55.179164
72	APARTMENTS_MEDI	50.749729
73	BASEMENTAREA_MEDI	58.515956
74	YEARS_BEGINEXPLUATATION_MEDI	48.781019
75	YEARS_BUILD_MEDI	66.497784
76	COMMONAREA_MEDI	69.872297
77	ELEVATORS_MEDI	53.295980
78	ENTRANCES_MEDI	50.348768
79	FLOORSMAX_MEDI	49.760822
80	FLOORSMIN_MEDI	67.848630
81	LANDAREA_MEDI	59.376738
82	LIVINGAPARTMENTS_MEDI	68.354953
83	LIVINGAREA_MEDI	50.193326
84	NONLIVINGAPARTMENTS_MEDI	69.432963
85	NONLIVINGAREA_MEDI	55.179164
86	FONDKAPREMONT_MODE	68.386172
87	HOUSETYPE_MODE	50.176091
88	TOTALAREA_MODE	48.268517
89	WALLSMATERIAL_MODE	50.840783
90	EMERGENCYSTATE_MODE	47.398304

	Column Name	Null Values Percentage
6	AMT_DOWN_PAYMENT	53.636480
12	RATE_DOWN_PAYMENT	53.636480
13	RATE_INTEREST_PRIMARY	99.643698
14	RATE_INTEREST_PRIVILEGED	99.643698
20	NAME_TYPE_SUITE	49.119754
31	DAYS_FIRST_DRAWING	40.298129
32	DAYS_FIRST_DUE	40.298129
33	DAYS_LAST_DUE_1ST_VERSION	40.298129
34	DAYS_LAST_DUE	40.298129
35	DAYS_TERMINATION	40.298129
36	NFLAG_INSURED_ON_APPROVAL	40.298129

	count	mean	std	min	25%	50%	75%	max
NAME_INCOME_TYPE
Businessman	10.0	6.525000	6.272260	1.8000	2.250	4.9500	8.43750	22.5000
Commercial associate	71617.0	2.029553	1.479742	0.2655	1.350	1.8000	2.25000	180.0009
Maternity leave	5.0	1.404000	1.268569	0.4950	0.675	0.9000	1.35000	3.6000
Pensioner	55362.0	1.364013	0.766503	0.2565	0.900	1.1700	1.66500	22.5000
State servant	21703.0	1.797380	1.008806	0.2700	1.125	1.5750	2.25000	31.5000
Student	18.0	1.705000	1.066447	0.8100	1.125	1.5750	1.78875	5.6250
Unemployed	22.0	1.105364	0.880551	0.2655	0.540	0.7875	1.35000	3.3750
Working	158774.0	1.631699	3.075777	0.2565	1.125	1.3500	2.02500	1170.0000

	VAR1	VAR2	Correlation
94	AMT_GOODS_PRICE	AMT_CREDIT	0.987250
230	CNT_FAM_MEMBERS	CNT_CHILDREN	0.878571
95	AMT_GOODS_PRICE	AMT_ANNUITY	0.776686
71	AMT_ANNUITY	AMT_CREDIT	0.771309
167	DAYS_EMPLOYED	DAYS_BIRTH	0.626114
70	AMT_ANNUITY	AMT_INCOME_TOTAL	0.418953
93	AMT_GOODS_PRICE	AMT_INCOME_TOTAL	0.349462
47	AMT_CREDIT	AMT_INCOME_TOTAL	0.342799
138	DAYS_BIRTH	CNT_CHILDREN	0.336966
190	DAYS_REGISTRATION	DAYS_BIRTH	0.333151

	VAR1	VAR2	Correlation
94	AMT_GOODS_PRICE	AMT_CREDIT	0.983103
230	CNT_FAM_MEMBERS	CNT_CHILDREN	0.885484
95	AMT_GOODS_PRICE	AMT_ANNUITY	0.752699
71	AMT_ANNUITY	AMT_CREDIT	0.752195
167	DAYS_EMPLOYED	DAYS_BIRTH	0.582185
190	DAYS_REGISTRATION	DAYS_BIRTH	0.289114
375	FLAG_DOCUMENT_3	DAYS_EMPLOYED	0.272169
335	DEF_60_CNT_SOCIAL_CIRCLE	OBS_60_CNT_SOCIAL_CIRCLE	0.264159
138	DAYS_BIRTH	CNT_CHILDREN	0.259109
213	DAYS_ID_PUBLISH	DAYS_BIRTH	0.252863

	SK_ID_CURR	TARGET	NAME_CONTRACT_TYPE_x	CODE_GENDER	FLAG_OWN_CAR	FLAG_OWN_REALTY	AMT_INCOME_TOTAL	AMT_CREDIT_x	AMT_ANNUITY_x	AMT_GOODS_PRICE_x	NAME_TYPE_SUITE	NAME_INCOME_TYPE	NAME_EDUCATION_TYPE	NAME_FAMILY_STATUS	NAME_HOUSING_TYPE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	DAYS_ID_PUBLISH	OCCUPATION_TYPE	CNT_FAM_MEMBERS	REGION_RATING_CLIENT	REGION_RATING_CLIENT_W_CITY	WEEKDAY_APPR_PROCESS_START	HOUR_APPR_PROCESS_START	ORGANIZATION_TYPE	OBS_30_CNT_SOCIAL_CIRCLE	DEF_30_CNT_SOCIAL_CIRCLE	OBS_60_CNT_SOCIAL_CIRCLE	DEF_60_CNT_SOCIAL_CIRCLE	DAYS_LAST_PHONE_CHANGE	FLAG_DOCUMENT_3	AMT_REQ_CREDIT_BUREAU_YEAR	AMT_INCOME_RANGE	AMT_CREDIT_RANGE	AGE	AGE_GROUP	YEARS_EMPLOYED	EMPLOYMENT_YEAR	SK_ID_PREV	NAME_CONTRACT_TYPE_y	AMT_ANNUITY_y	AMT_APPLICATION	AMT_CREDIT_y	AMT_GOODS_PRICE_y	NAME_CASH_LOAN_PURPOSE	NAME_CONTRACT_STATUS	DAYS_DECISION	NAME_PAYMENT_TYPE	CODE_REJECT_REASON	NAME_CLIENT_TYPE	NAME_GOODS_CATEGORY	NAME_PORTFOLIO	NAME_PRODUCT_TYPE	CHANNEL_TYPE	SELLERPLACE_AREA	NAME_SELLER_INDUSTRY	CNT_PAYMENT	NAME_YIELD_GROUP	PRODUCT_COMBINATION	DAYS_DECISION_GROUP
0	100002	1	Cash loans	M	N	Y	2.025	4.065975	24700.5	351000.0	Unaccompanied	Working	Secondary / secondary special	Single / not married	House / apartment	0.018801	9461	637	3648.0	2120	Laborers	1.0	2	2	WEDNESDAY	10	Business Entity Type 3	2.0	2.0	2.0	2.0	-1134.0	1	1.0	200k-300k	400k-500k	25	20-30	1	0-5	1038818	Consumer loans	9251.775	179055.0	179055.0	179055.0	XAP	Approved	606	XNA	XAP	New	Vehicles	POS	XNA	Stone	500	Auto technology	24.0	low_normal	POS other with interest	400-800
1	100003	0	Cash loans	F	N	N	2.700	12.935025	35698.5	1129500.0	Family	State servant	Higher education	Married	House / apartment	0.003541	16765	1188	1186.0	291	Core staff	2.0	1	1	MONDAY	11	School	1.0	0.0	1.0	0.0	-828.0	1	0.0	200k-300k	1M Above	45	40-50	3	0-5	1810518	Cash loans	98356.995	900000.0	1035882.0	900000.0	XNA	Approved	746	XNA	XAP	Repeater	XNA	Cash	x-sell	Credit and cash offices	-1	XNA	12.0	low_normal	Cash X-Sell: low	400-800
2	100003	0	Cash loans	F	N	N	2.700	12.935025	35698.5	1129500.0	Family	State servant	Higher education	Married	House / apartment	0.003541	16765	1188	1186.0	291	Core staff	2.0	1	1	MONDAY	11	School	1.0	0.0	1.0	0.0	-828.0	1	0.0	200k-300k	1M Above	45	40-50	3	0-5	2636178	Consumer loans	64567.665	337500.0	348637.5	337500.0	XAP	Approved	828	Cash through the bank	XAP	Refreshed	Furniture	POS	XNA	Stone	1400	Furniture	6.0	middle	POS industry with interest	800-1200
3	100003	0	Cash loans	F	N	N	2.700	12.935025	35698.5	1129500.0	Family	State servant	Higher education	Married	House / apartment	0.003541	16765	1188	1186.0	291	Core staff	2.0	1	1	MONDAY	11	School	1.0	0.0	1.0	0.0	-828.0	1	0.0	200k-300k	1M Above	45	40-50	3	0-5	2396755	Consumer loans	6737.310	68809.5	68053.5	68809.5	XAP	Approved	2341	Cash through the bank	XAP	Refreshed	Consumer Electronics	POS	XNA	Country-wide	200	Consumer electronics	12.0	middle	POS household with interest	2000-2400
4	100004	0	Revolving loans	M	Y	Y	0.675	1.350000	6750.0	135000.0	Unaccompanied	Working	Secondary / secondary special	Single / not married	House / apartment	0.010032	19046	225	4260.0	2531	Laborers	1.0	2	2	MONDAY	9	Government	0.0	0.0	0.0	0.0	-815.0	0	0.0	0-100K	100K-200K	52	50 above	0	NaN	1564014	Consumer loans	5357.250	24282.0	20106.0	24282.0	XAP	Approved	815	Cash through the bank	XAP	New	Mobile	POS	XNA	Regional / Local	30	Connectivity	4.0	middle	POS mobile without interest	800-1200

	SK_ID_CURR	TARGET	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT_x	AMT_ANNUITY_x	AMT_GOODS_PRICE_x	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	DAYS_ID_PUBLISH	CNT_FAM_MEMBERS	HOUR_APPR_PROCESS_START	REG_REGION_NOT_LIVE_REGION	OBS_30_CNT_SOCIAL_CIRCLE	DEF_30_CNT_SOCIAL_CIRCLE	OBS_60_CNT_SOCIAL_CIRCLE	DEF_60_CNT_SOCIAL_CIRCLE	DAYS_LAST_PHONE_CHANGE	FLAG_DOCUMENT_3	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR	AGE	YEARS_EMPLOYED	SK_ID_PREV	AMT_ANNUITY_y	AMT_APPLICATION	AMT_CREDIT_y	AMT_GOODS_PRICE_y	DAYS_DECISION	SELLERPLACE_AREA	CNT_PAYMENT
count	1.413701e+06	1.413701e+06	1.413701e+06	1.413701e+06	1.413701e+06	1.413608e+06	1.412493e+06	1.413701e+06	1.413701e+06	1.413701e+06	1.413701e+06	1.413701e+06	1.413701e+06	1.413701e+06	1.413701e+06	1.410555e+06	1.410555e+06	1.410555e+06	1.410555e+06	1.413701e+06	1.413701e+06	1.413701e+06	1.413701e+06	1.413701e+06	1.413701e+06	1.413701e+06	1.413701e+06	1.413701e+06	1.413701e+06	1.413701e+06	1.413701e+06	1.413701e+06	1.413700e+06	1.413701e+06	1.413701e+06	1.413701e+06	1.106488e+06
mean	2.784813e+05	8.655296e-02	4.048933e-01	1.733160e+00	5.875537e+00	2.701702e+04	5.277186e+05	2.074985e-02	1.632105e+04	7.266347e+04	5.003233e+03	3.034563e+03	2.150501e+00	1.198433e+01	1.207327e-02	1.544176e+00	1.540436e-01	1.526303e+00	1.080426e-01	-1.084701e+03	7.385600e-01	5.484894e-03	6.028149e-03	3.410198e-02	2.664913e-01	3.196935e-01	2.691239e+00	4.421384e+01	1.985500e+02	1.922744e+06	1.484032e+04	1.752436e+05	1.963541e+05	1.854396e+05	8.803670e+02	3.149878e+02	1.605194e+01
std	1.028118e+05	2.811789e-01	7.173454e-01	1.985734e+00	3.849173e+00	1.395116e+04	3.532465e+05	1.334702e-02	4.344557e+03	1.433374e+05	3.551051e+03	1.507376e+03	9.006787e-01	3.232181e+00	1.092132e-01	2.530715e+00	4.658973e-01	2.508953e+00	3.790588e-01	7.999369e+02	4.394192e-01	7.702591e-02	1.001966e-01	2.012902e-01	9.268428e-01	8.781444e-01	2.157176e+00	1.190217e+01	3.926378e+02	5.327153e+05	1.316370e+04	2.936222e+05	3.194813e+05	2.881244e+05	7.835402e+02	7.695082e+03	1.456675e+01
min	1.000020e+05	0.000000e+00	0.000000e+00	2.565000e-01	4.500000e-01	1.615500e+03	4.050000e+04	2.900000e-04	7.489000e+03	0.000000e+00	0.000000e+00	0.000000e+00	1.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	-4.292000e+03	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	2.000000e+01	0.000000e+00	1.000001e+06	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	1.000000e+00	-1.000000e+00	0.000000e+00
25%	1.893640e+05	0.000000e+00	0.000000e+00	1.125000e+00	2.700000e+00	1.682100e+04	2.385000e+05	1.003200e-02	1.273900e+04	1.042000e+03	2.001000e+03	1.783000e+03	2.000000e+00	1.000000e+01	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	-1.683000e+03	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	1.000000e+00	3.400000e+01	2.000000e+00	1.461346e+06	7.406055e+03	1.975050e+04	2.488050e+04	4.500000e+04	2.710000e+02	-1.000000e+00	6.000000e+00
50%	2.789920e+05	0.000000e+00	0.000000e+00	1.575000e+00	5.084955e+00	2.492550e+04	4.500000e+05	1.885000e-02	1.604400e+04	2.401000e+03	4.508000e+03	3.330000e+03	2.000000e+00	1.200000e+01	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	-1.011000e+03	1.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	2.000000e+00	4.300000e+01	6.000000e+00	1.922698e+06	1.125000e+04	7.087050e+04	8.059500e+04	7.087500e+04	5.820000e+02	4.000000e+00	1.200000e+01
75%	3.675560e+05	0.000000e+00	1.000000e+00	2.070000e+00	8.079840e+00	3.454200e+04	6.795000e+05	2.866300e-02	1.998000e+04	6.313000e+03	7.510000e+03	4.319000e+03	3.000000e+00	1.400000e+01	0.000000e+00	2.000000e+00	0.000000e+00	2.000000e+00	0.000000e+00	-3.960000e+02	1.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	4.000000e+00	5.400000e+01	1.700000e+01	2.384012e+06	1.674797e+04	1.800000e+05	2.156400e+05	1.800000e+05	1.313000e+03	8.500000e+01	2.400000e+01
max	4.562550e+05	1.000000e+00	1.900000e+01	1.170000e+03	4.050000e+01	2.250000e+05	4.050000e+06	7.250800e-02	2.520100e+04	3.652430e+05	2.467200e+04	7.197000e+03	2.000000e+01	2.300000e+01	1.000000e+00	3.480000e+02	3.400000e+01	3.440000e+02	2.400000e+01	0.000000e+00	1.000000e+00	4.000000e+00	9.000000e+00	8.000000e+00	2.700000e+01	2.610000e+02	2.500000e+01	6.900000e+01	1.000000e+03	2.845381e+06	4.180581e+05	5.850000e+06	4.509688e+06	5.850000e+06	2.922000e+03	4.000000e+06	8.400000e+01